Essec\Faculty\Model\Profile {#2216
#_id: "B00732676"
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"bid" => "B00732676"
"academId" => "2049"
"slug" => "klopp-olga"
"fullName" => "Olga KLOPP"
"lastName" => "KLOPP"
"firstName" => "Olga"
"title" => array:2 [
"fr" => "Professeur"
"en" => "Professor"
]
"email" => "b00732676@essec.edu"
"status" => "ACTIF"
"campus" => "Campus de Cergy"
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"phone" => "+33 (0)1 34 43 36 98"
"sites" => []
"facNumber" => "2049"
"externalCvUrl" => "https://faculty.essec.edu/en/cv/klopp-olga/pdf"
"googleScholarUrl" => "https://scholar.google.com/citations?hl=fr&user=ZzU7Y-EAAAAJ"
"facOrcId" => "https://orcid.org/0000-0002-7195-0690"
"career" => array:8 [
0 => Essec\Faculty\Model\CareerItem {#2225
#_index: null
#_id: null
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"en" => "ESSEC Business School"
]
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]
]
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}
1 => Essec\Faculty\Model\CareerItem {#2226
#_index: null
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#_source: array:7 [
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]
"country" => array:2 [
"fr" => "France"
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]
]
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}
2 => Essec\Faculty\Model\CareerItem {#2227
#_index: null
#_id: null
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"en" => "Centre de recherche en économie et statistique (CREST)"
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"country" => array:2 [
"fr" => "France"
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]
]
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}
3 => Essec\Faculty\Model\CareerItem {#2228
#_index: null
#_id: null
#_source: array:7 [
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"en" => "Université Nationale Autonome de Mexico"
]
"country" => array:2 [
"fr" => "Mexique"
"en" => "Mexico"
]
]
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}
4 => Essec\Faculty\Model\CareerItem {#2229
#_index: null
#_id: null
#_source: array:7 [
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"en" => "Centre de recherche en économie et statistique (CREST)"
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}
5 => Essec\Faculty\Model\CareerItem {#2230
#_index: null
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}
6 => Essec\Faculty\Model\CareerItem {#2231
#_index: null
#_id: null
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7 => Essec\Faculty\Model\CareerItem {#2232
#_index: null
#_id: null
#_source: array:7 [
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}
]
"diplomes" => array:3 [
0 => Essec\Faculty\Model\Diplome {#2218
#_index: null
#_id: null
#_source: array:6 [
"diplome" => "DIPLOMA"
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"fr" => "Diplômes"
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"country" => array:2 [
"fr" => "Mexique"
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]
]
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}
1 => Essec\Faculty\Model\Diplome {#2220
#_index: null
#_id: null
#_source: array:6 [
"diplome" => "DIPLOMA"
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"en" => "Université d'État Lomonossov de Moscou"
]
"country" => array:2 [
"fr" => "Russie"
"en" => "Russia"
]
]
+lang: "en"
+"parent": Essec\Faculty\Model\Profile {#2216}
}
2 => Essec\Faculty\Model\Diplome {#2217
#_index: null
#_id: null
#_source: array:6 [
"diplome" => "DIPLOMA"
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]
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]
]
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}
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"bio" => array:2 [
"fr" => "<p>2017 Professeur associé ESSEC Business School</p><p>2017 Délégation CNRS </p><p>2012 - 2017 Maîre de conférences Université Paris Ouest Nanterre la Défense</p><p>2010 - 2012 Post-Doctorant au CREST, Paris </p><p>2004 - 2008 Maître de conférences Université Nationale Autonome de Mexico (UNAM) </p><p> </p>"
"en" => "<p>2017 Associated Professor at ESSEC Business School, Cergy </p><p>2017 Member of CNRS (6 month) at CREST, Paris </p><p>2012 - 2017 Assistant Professor University Paris Ouest Nanterre la Défense</p><p>2010 - 2012 Postdoctoral fellow in CREST, Paris</p><p>2004 - 2008 Assistant Professor UNAM (Mexico) </p>"
]
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]
"site" => array:2 [
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0 => Essec\Faculty\Model\TeachingItem {#2223
#_index: null
#_id: null
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}
1 => Essec\Faculty\Model\TeachingItem {#2224
#_index: null
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#_source: array:7 [
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}
2 => Essec\Faculty\Model\TeachingItem {#2222
#_index: null
#_id: null
#_source: array:7 [
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]
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}
3 => Essec\Faculty\Model\TeachingItem {#2219
#_index: null
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"country" => array:2 [
"fr" => "Russie"
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]
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}
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0 => Essec\Faculty\Model\ExtraActivity {#2221
#_index: null
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]
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}
1 => Essec\Faculty\Model\ExtraActivity {#2215
#_index: null
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0 => Essec\Faculty\Model\Contribution {#2234
#_index: "academ_contributions"
#_id: "12807"
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"slug" => "network-models-and-sparse-graphon-estimation"
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"title" => "Network Models and Sparse Graphon Estimation"
"description" => "KLOPP, O. et GAUCHER, S. (2021). Network Models and Sparse Graphon Estimation. Dans: 2021 Crimean Autumn Mathematical School-Symposium. Satera."
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1 => Essec\Faculty\Model\Contribution {#2236
#_index: "academ_contributions"
#_id: "7091"
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"slug" => "robust-matrix-completion-collective-matrix-completion"
"yearMonth" => "2018-06"
"year" => "2018"
"title" => "Robust Matrix Completion/Collective Matrix Completion"
"description" => "KLOPP, O., LOUNICI, K., TSYBAKOV, A.B. et ALAYA, M.Z. (2018). Robust Matrix Completion/Collective Matrix Completion. Dans: 40th Conference on Stochastic Processes and their Applications (SPA 2018)."
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1 => array:1 [
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2 => array:1 [
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3 => array:1 [
"name" => "ALAYA M. Z."
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"ouvrage" => "40th Conference on Stochastic Processes and their Applications (SPA 2018)"
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2 => Essec\Faculty\Model\Contribution {#2238
#_index: "academ_contributions"
#_id: "14215"
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"slug" => "optimality-of-variational-inference-for-stochastic-block-model"
"yearMonth" => "2022-09"
"year" => "2022"
"title" => "Optimality of Variational Inference for Stochastic Block Model"
"description" => "GAUCHER, S. et KLOPP, O. (2022). Optimality of Variational Inference for Stochastic Block Model. Dans: 2022 Graph Limits, Nonparametric Models, and Estimation. Berkeley."
"authors" => array:2 [
0 => array:3 [
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1 => array:1 [
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"ouvrage" => "2022 Graph Limits, Nonparametric Models, and Estimation"
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+lang: "en"
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3 => Essec\Faculty\Model\Contribution {#2235
#_index: "academ_contributions"
#_id: "14217"
#_source: array:18 [
"id" => "14217"
"slug" => "outlier-detection-in-networks"
"yearMonth" => "2022-06"
"year" => "2022"
"title" => "Outlier Detection in Networks"
"description" => "GAUCHER, S., KLOPP, O. et ROBIN, G. (2022). Outlier Detection in Networks. Dans: 2022 International Symposium on Nonparametric Statistics (ISNPS). Paphos."
"authors" => array:3 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
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1 => array:1 [
"name" => "GAUCHER S"
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2 => array:1 [
"name" => "ROBIN Geneviève"
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"ouvrage" => "2022 International Symposium on Nonparametric Statistics (ISNPS)"
"keywords" => []
"updatedAt" => "2023-07-21 01:00:39"
"publicationUrl" => null
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4 => Essec\Faculty\Model\Contribution {#2239
#_index: "academ_contributions"
#_id: "14354"
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"id" => "14354"
"slug" => "optimality-of-variational-inference-for-stochastic-block-model"
"yearMonth" => "2023-09"
"year" => "2023"
"title" => "Optimality of Variational Inference for Stochastic Block Model"
"description" => "GAUCHER, S. et KLOPP, O. (2023). Optimality of Variational Inference for Stochastic Block Model. Dans: Workshop on Eco-Stat Asymptotics 2023 WESA2023. Verona."
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]
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5 => Essec\Faculty\Model\Contribution {#2233
#_index: "academ_contributions"
#_id: "14401"
#_source: array:18 [
"id" => "14401"
"slug" => "change-point-detection-in-dynamic-networks"
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"year" => "2023"
"title" => "Change Point Detection in Dynamic Networks"
"description" => "ENIKEEVA, F. et KLOPP, O. (2023). Change Point Detection in Dynamic Networks. Dans: 2023 Change Point Workshop at Warwick. Warwick."
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1 => array:1 [
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"authors_fields" => array:2 [
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"en" => "Information Systems, Data Analytics and Operations"
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"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
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}
6 => Essec\Faculty\Model\Contribution {#2237
#_index: "academ_contributions"
#_id: "14425"
#_source: array:18 [
"id" => "14425"
"slug" => "optimality-of-variational-inference-for-stochastic-block-model"
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"year" => "2023"
"title" => "Optimality of Variational Inference for Stochastic Block Model"
"description" => "GAUCHER, S. et KLOPP, O. (2023). Optimality of Variational Inference for Stochastic Block Model. Dans: 12th Workshop on High Dimensional Data Analysis (HDDA-XII) 2023. Paris."
"authors" => array:2 [
0 => array:3 [
"name" => "KLOPP Olga"
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1 => array:1 [
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"ouvrage" => "12th Workshop on High Dimensional Data Analysis (HDDA-XII) 2023"
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"support_type" => array:2 [
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]
+lang: "en"
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}
7 => Essec\Faculty\Model\Contribution {#2240
#_index: "academ_contributions"
#_id: "14664"
#_source: array:18 [
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"slug" => "denoising-over-network-with-application-to-descrete-time-epidemic-process"
"yearMonth" => "2023-12"
"year" => "2023"
"title" => "Denoising Over Network with Application to Descrete-time Epidemic Process"
"description" => "KLOPP, O. (2023). Denoising Over Network with Application to Descrete-time Epidemic Process. Dans: 2023 Graph Limits and Processes on Networks Reunion. Berkeley."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
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"ouvrage" => "2023 Graph Limits and Processes on Networks Reunion"
"keywords" => []
"updatedAt" => "2024-01-31 01:00:38"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => ""
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"type" => array:2 [
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"support_type" => array:2 [
"fr" => null
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"countries" => array:2 [
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"abstract" => array:2 [
"fr" => ""
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]
"authors_fields" => array:2 [
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"en" => "Information Systems, Data Analytics and Operations"
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"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
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8 => Essec\Faculty\Model\Contribution {#2241
#_index: "academ_contributions"
#_id: "10573"
#_source: array:18 [
"id" => "10573"
"slug" => "1-bit-matrix-completion"
"yearMonth" => "2015-01"
"year" => "2015"
"title" => "1-bit Matrix Completion"
"description" => "KLOPP, O. (2015). 1-bit Matrix Completion. Dans: Indian Russian Conference in Statistics and Probability. Delhi."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
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"ouvrage" => "Indian Russian Conference in Statistics and Probability"
"keywords" => []
"updatedAt" => "2021-07-13 14:31:38"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
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"type" => array:2 [
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"support_type" => array:2 [
"fr" => null
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"countries" => array:2 [
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"abstract" => array:2 [
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"authors_fields" => array:2 [
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"en" => "Information Systems, Data Analytics and Operations"
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"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
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}
9 => Essec\Faculty\Model\Contribution {#2242
#_index: "academ_contributions"
#_id: "10606"
#_source: array:18 [
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"slug" => "oracle-inequalities-for-network-models-and-sparse-graphon-estimation"
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"year" => "2015"
"title" => "Oracle inequalities for network models and sparse graphon estimation"
"description" => "KLOPP, O. (2015). Oracle inequalities for network models and sparse graphon estimation. Dans: Meeting in Mathematical Statistics. Frejus."
"authors" => array:1 [
0 => array:3 [
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"ouvrage" => "Meeting in Mathematical Statistics"
"keywords" => []
"updatedAt" => "2021-07-13 14:31:39"
"publicationUrl" => null
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"pages" => null
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"support_type" => array:2 [
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"countries" => array:2 [
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"abstract" => array:2 [
"fr" => null
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"authors_fields" => array:2 [
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"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
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}
10 => Essec\Faculty\Model\Contribution {#2243
#_index: "academ_contributions"
#_id: "10607"
#_source: array:18 [
"id" => "10607"
"slug" => "oracle-inequalities-for-network-models-and-sparse-graphon-estimation"
"yearMonth" => "2015-11"
"year" => "2015"
"title" => "Oracle inequalities for network models and sparse graphon estimation"
"description" => "KLOPP, O. (2015). Oracle inequalities for network models and sparse graphon estimation. Dans: 2nd Heidelberg - Mannheim Stochastic Colloquium."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
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"ouvrage" => "2nd Heidelberg - Mannheim Stochastic Colloquium"
"keywords" => []
"updatedAt" => "2021-07-13 14:31:39"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
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"type" => array:2 [
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"support_type" => array:2 [
"fr" => null
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"abstract" => array:2 [
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"authors_fields" => array:2 [
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"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
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}
11 => Essec\Faculty\Model\Contribution {#2244
#_index: "academ_contributions"
#_id: "10626"
#_source: array:18 [
"id" => "10626"
"slug" => "confidence-sets-for-matrix-completion"
"yearMonth" => "2016-12"
"year" => "2016"
"title" => "Confidence sets for matrix completion"
"description" => "KLOPP, O. (2016). Confidence sets for matrix completion. Dans: Advances in nonparametric and high-dimensional Statistic. Frejus."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
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"ouvrage" => "Advances in nonparametric and high-dimensional Statistic"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
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"type" => array:2 [
"fr" => "Communications dans une conférence"
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"support_type" => array:2 [
"fr" => null
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"countries" => array:2 [
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"abstract" => array:2 [
"fr" => null
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"authors_fields" => array:2 [
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"en" => "Information Systems, Data Analytics and Operations"
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"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
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}
12 => Essec\Faculty\Model\Contribution {#2245
#_index: "academ_contributions"
#_id: "10639"
#_source: array:18 [
"id" => "10639"
"slug" => "matrix-completion"
"yearMonth" => "2016-04"
"year" => "2016"
"title" => "Matrix Completion"
"description" => "KLOPP, O. (2016). Matrix Completion. Dans: Multimedia Inpainting Workshop. Rennes."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
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"ouvrage" => "Multimedia Inpainting Workshop"
"keywords" => []
"updatedAt" => "2021-07-13 14:31:40"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
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"support_type" => array:2 [
"fr" => null
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"countries" => array:2 [
"fr" => null
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"abstract" => array:2 [
"fr" => null
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"authors_fields" => array:2 [
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"en" => "Information Systems, Data Analytics and Operations"
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"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
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}
13 => Essec\Faculty\Model\Contribution {#2246
#_index: "academ_contributions"
#_id: "10641"
#_source: array:18 [
"id" => "10641"
"slug" => "network-models-and-sparse-graphon-estimation"
"yearMonth" => "2016-12"
"year" => "2016"
"title" => "Network models and sparse graphon estimation."
"description" => "KLOPP, O. (2016). Network models and sparse graphon estimation. Dans: NIPS workshop. Barcelona."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
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"ouvrage" => "NIPS workshop"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
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"type" => array:2 [
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"support_type" => array:2 [
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"authors_fields" => array:2 [
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"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
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}
14 => Essec\Faculty\Model\Contribution {#2247
#_index: "academ_contributions"
#_id: "10646"
#_source: array:18 [
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"slug" => "oracle-inequalities-for-network-models-and-sparse-graphon-estimation"
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"year" => "2016"
"title" => "Oracle inequalities for network models and sparse graphon estimation"
"description" => "KLOPP, O. (2016). Oracle inequalities for network models and sparse graphon estimation. Dans: Joint Statistical Meeting. Chicago."
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+lang: "en"
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15 => Essec\Faculty\Model\Contribution {#2248
#_index: "academ_contributions"
#_id: "10647"
#_source: array:18 [
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"slug" => "oracle-inequalities-for-network-models-and-sparse-graphon-estimation"
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"title" => "Oracle inequalities for network models and sparse graphon estimation"
"description" => "KLOPP, O. (2016). Oracle inequalities for network models and sparse graphon estimation. Dans: 3rd ISNPS conference. Avignon."
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+lang: "en"
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16 => Essec\Faculty\Model\Contribution {#2249
#_index: "academ_contributions"
#_id: "10648"
#_source: array:18 [
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"slug" => "oracle-inequalities-for-network-models-and-sparse-graphon-estimation"
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"year" => "2016"
"title" => "Oracle inequalities for network models and sparse graphon estimation"
"description" => "KLOPP, O. (2016). Oracle inequalities for network models and sparse graphon estimation. Dans: Modern problems of stochastic analysis and statistics. Moscow."
"authors" => array:1 [
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"keywords" => []
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"publicationUrl" => null
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+lang: "en"
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17 => Essec\Faculty\Model\Contribution {#2250
#_index: "academ_contributions"
#_id: "10676"
#_source: array:18 [
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"slug" => "optimal-graphon-estimation-in-cut-distance"
"yearMonth" => "2017-07"
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"title" => "Optimal graphon estimation in cut distance"
"description" => "KLOPP, O. (2017). Optimal graphon estimation in cut distance. Dans: The Foundations in Computational Mathematics Conference. Barcelona."
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"ouvrage" => "The Foundations in Computational Mathematics Conference"
"keywords" => []
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"publicationUrl" => null
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"type" => array:2 [
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"authors_fields" => array:2 [
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"indexedAt" => "2024-11-21T08:21:48.000Z"
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+lang: "en"
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18 => Essec\Faculty\Model\Contribution {#2251
#_index: "academ_contributions"
#_id: "10677"
#_source: array:18 [
"id" => "10677"
"slug" => "optimal-graphon-estimation-in-cut-distance"
"yearMonth" => "2017-06"
"year" => "2017"
"title" => "Optimal graphon estimation in cut distance"
"description" => "KLOPP, O. (2017). Optimal graphon estimation in cut distance. Dans: Statistics meets Stochastics 2. Moscow."
"authors" => array:1 [
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"name" => "KLOPP Olga"
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"ouvrage" => "Statistics meets Stochastics 2"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
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"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => null
"en" => null
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
19 => Essec\Faculty\Model\Contribution {#2252
#_index: "academ_contributions"
#_id: "10688"
#_source: array:18 [
"id" => "10688"
"slug" => "network-models"
"yearMonth" => "2018-08"
"year" => "2018"
"title" => "Network models"
"description" => "KLOPP, O., TSYBAKOV, A. et VERZELEN, N. (2018). Network models. Dans: Tercera jornada Franco-Chilena de Estadística. Valparaiso."
"authors" => array:3 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "TSYBAKOV Alexandre"
]
2 => array:1 [
"name" => "VERZELEN Nicolas"
]
]
"ouvrage" => "Tercera jornada Franco-Chilena de Estadística"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => null
"en" => null
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
20 => Essec\Faculty\Model\Contribution {#2253
#_index: "academ_contributions"
#_id: "10726"
#_source: array:18 [
"id" => "10726"
"slug" => "modeles-de-reseaux-parcimonieux"
"yearMonth" => "2019-09"
"year" => "2019"
"title" => "Modèles de réseaux parcimonieux"
"description" => "KLOPP, O. (2019). Modèles de réseaux parcimonieux. Dans: 2019 Colloquium de Montpellier."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
]
"ouvrage" => "2019 Colloquium de Montpellier"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => null
"en" => null
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
21 => Essec\Faculty\Model\Contribution {#2254
#_index: "academ_contributions"
#_id: "10871"
#_source: array:18 [
"id" => "10871"
"slug" => "link-prediction-in-sparse-graphon-model"
"yearMonth" => "2020-04"
"year" => "2020"
"title" => "Link Prediction in Sparse Graphon Model"
"description" => "KLOPP, O. (2020). Link Prediction in Sparse Graphon Model. Dans: 2020 EURANDOM Workshop: Graph Limits."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
]
"ouvrage" => "2020 EURANDOM Workshop: Graph Limits"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => null
"en" => null
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
22 => Essec\Faculty\Model\Contribution {#2255
#_index: "academ_contributions"
#_id: "10972"
#_source: array:18 [
"id" => "10972"
"slug" => "robust-network-analysis"
"yearMonth" => "2020-12"
"year" => "2020"
"title" => "Robust network analysis"
"description" => "KLOPP, O. (2020). Robust network analysis. Dans: 13th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2020), Virtual Conference."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
]
"ouvrage" => "13th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2020), Virtual Conference"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => null
"en" => null
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
23 => Essec\Faculty\Model\Contribution {#2256
#_index: "academ_contributions"
#_id: "4833"
#_source: array:18 [
"id" => "4833"
"slug" => "low-rank-interactions-and-sparse-additive-effects-model-for-large-data-frames"
"yearMonth" => "2018-12"
"year" => "2018"
"title" => "Low-Rank Interactions and Sparse Additive Effects Model for Large Data Frames"
"description" => "ROBIN, G., WAI, H.T., JOSSE, J., KLOPP, O. et MOULINES, A. (2018). Low-Rank Interactions and Sparse Additive Effects Model for Large Data Frames. Dans: <i>Advances in Neural Information Processing Systems 31 (NIPS 2018)</i>. "
"authors" => array:5 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "ROBIN Geneviève"
]
2 => array:1 [
"name" => "WAI H.-T."
]
3 => array:1 [
"name" => "JOSSE J."
]
4 => array:1 [
"name" => "MOULINES A. É."
]
]
"ouvrage" => "Advances in Neural Information Processing Systems 31 (NIPS 2018)"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Actes d'une conférence"
"en" => "Conference Proceedings"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "Many applications of machine learning involve the analysis of large data frames – matrices collecting heterogeneous measurements (binary, numerical, counts, etc.) across samples – with missing values. Low-rank models are popular in this framework for tasks such as visualization, clustering and missing value imputation. Yet, available methods with statistical guarantees and efficient optimization do not allow explicit modeling of main additive effects such as row and column, or covariate effects. In this paper, we introduce a low- rank interaction and sparse additive effects (LORIS) model which combines matrix regression on a dictionary and low-rank design, to estimate main effects and interactions simultaneously. We provide statistical guarantees in the form of upper bounds on the estimation error of both components. Then, we introduce a mixed coordinate gradient descent (MCGD) method which provably converges sub-linearly to an optimal solution and is computationally efficient for large scale data sets. We show on simulated and survey data that the method has a clear advantage over current practices."
"en" => "Many applications of machine learning involve the analysis of large data frames – matrices collecting heterogeneous measurements (binary, numerical, counts, etc.) across samples – with missing values. Low-rank models are popular in this framework for tasks such as visualization, clustering and missing value imputation. Yet, available methods with statistical guarantees and efficient optimization do not allow explicit modeling of main additive effects such as row and column, or covariate effects. In this paper, we introduce a low- rank interaction and sparse additive effects (LORIS) model which combines matrix regression on a dictionary and low-rank design, to estimate main effects and interactions simultaneously. We provide statistical guarantees in the form of upper bounds on the estimation error of both components. Then, we introduce a mixed coordinate gradient descent (MCGD) method which provably converges sub-linearly to an optimal solution and is computationally efficient for large scale data sets. We show on simulated and survey data that the method has a clear advantage over current practices."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
24 => Essec\Faculty\Model\Contribution {#2257
#_index: "academ_contributions"
#_id: "1992"
#_source: array:18 [
"id" => "1992"
"slug" => "main-effects-and-interactions-in-mixed-and-incomplete-data-frames"
"yearMonth" => "2019-05"
"year" => "2019"
"title" => "Main Effects and Interactions in Mixed and Incomplete Data Frames"
"description" => "ROBIN, G., KLOPP, O., JOSSE, J., MOULINES, E. et TIBSHIRANI, R. (2019). Main Effects and Interactions in Mixed and Incomplete Data Frames. <i>Journal of the American Statistical Association</i>, 115(531), pp. 1292-1303."
"authors" => array:5 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "ROBIN Geneviève"
]
2 => array:1 [
"name" => "JOSSE J."
]
3 => array:1 [
"name" => "MOULINES E."
]
4 => array:1 [
"name" => "TIBSHIRANI R."
]
]
"ouvrage" => ""
"keywords" => array:3 [
0 => "Heterogeneous data"
1 => "Low-rank matrix completion"
2 => "Missing values"
]
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://www.tandfonline.com/doi/abs/10.1080/01621459.2019.1623041?journalCode=uasa20"
"publicationInfo" => array:3 [
"pages" => "1292-1303"
"volume" => "115"
"number" => "531"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
A mixed data frame (MDF) is a table collecting categorical, numerical and count observations. The use of MDF is widespread in statistics and the applications are numerous from abundance data in ecology to recommender systems. In many cases, an MDF exhibits simultaneously main effects, such as row, column or group effects and interactions, for which a low-rank model has often been suggested. Although the literature on low-rank approximations is very substantial, with few exceptions, existing methods do not allow to incorporate main effects and interactions while providing statistical guarantees. The present work fills this gap. We propose an estimation method which allows to recover\n
\n
simultaneously the main effects and the interactions. We show that our method is near optimal under conditions which are met in our targeted applications. We also propose an optimization algorithm which provably converges to an optimal solution. Numerical experiments reveal that our method, mimi, performs well when the main effects are sparse and the interaction matrix has low-rank. We also show that mimi compares favorably to existing methods, in particular when the main effects are significantly large compared to the interactions, and when the proportion of missing entries is large. The method is available as an R package on the Comprehensive R Archive Network.
"""
"en" => """
A mixed data frame (MDF) is a table collecting categorical, numerical and count observations. The use of MDF is widespread in statistics and the applications are numerous from abundance data in ecology to recommender systems. In many cases, an MDF exhibits simultaneously main effects, such as row, column or group effects and interactions, for which a low-rank model has often been suggested. Although the literature on low-rank approximations is very substantial, with few exceptions, existing methods do not allow to incorporate main effects and interactions while providing statistical guarantees. The present work fills this gap. We propose an estimation method which allows to recover\n
\n
simultaneously the main effects and the interactions. We show that our method is near optimal under conditions which are met in our targeted applications. We also propose an optimization algorithm which provably converges to an optimal solution. Numerical experiments reveal that our method, mimi, performs well when the main effects are sparse and the interaction matrix has low-rank. We also show that mimi compares favorably to existing methods, in particular when the main effects are significantly large compared to the interactions, and when the proportion of missing entries is large. The method is available as an R package on the Comprehensive R Archive Network.
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
25 => Essec\Faculty\Model\Contribution {#2258
#_index: "academ_contributions"
#_id: "2158"
#_source: array:18 [
"id" => "2158"
"slug" => "optimal-graphon-estimation-in-cut-distance"
"yearMonth" => "2018-10"
"year" => "2018"
"title" => "Optimal Graphon Estimation in Cut Distance"
"description" => "KLOPP, O. et VERZELEN, N. (2018). Optimal Graphon Estimation in Cut Distance. <i>Probability Theory and Related Fields</i>, 174, pp. 1033-1090."
"authors" => array:2 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "VERZELEN N."
]
]
"ouvrage" => ""
"keywords" => array:6 [
0 => "Inhomogeneous random graph"
1 => "Graphon"
2 => "W-random graphs"
3 => "Networks"
4 => "Stochastic block model"
5 => "Cut distance"
]
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://link.springer.com/article/10.1007%2Fs00440-018-0878-1"
"publicationInfo" => array:3 [
"pages" => "1033-1090"
"volume" => "174"
"number" => null
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "Consider the twin problems of estimating the connection probability matrix of an inhomogeneous random graph and the graphon of a W-random graph. We establish the minimax estimation rates with respect to the cut metric for classes of block constant matrices and step function graphons. Surprisingly, our results imply that, from the minimax point of view, the raw data, that is, the adjacency matrix of the observed graph, is already optimal and more involved procedures cannot improve the convergence rates for this metric. This phenomenon contrasts with optimal rates of convergence with respect to other classical distances for graphons such as the l1 or l2 metrics."
"en" => "Consider the twin problems of estimating the connection probability matrix of an inhomogeneous random graph and the graphon of a W-random graph. We establish the minimax estimation rates with respect to the cut metric for classes of block constant matrices and step function graphons. Surprisingly, our results imply that, from the minimax point of view, the raw data, that is, the adjacency matrix of the observed graph, is already optimal and more involved procedures cannot improve the convergence rates for this metric. This phenomenon contrasts with optimal rates of convergence with respect to other classical distances for graphons such as the l1 or l2 metrics."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
26 => Essec\Faculty\Model\Contribution {#2259
#_index: "academ_contributions"
#_id: "6658"
#_source: array:18 [
"id" => "6658"
"slug" => "matrix-completion-old-and-new"
"yearMonth" => "2019-08"
"year" => "2019"
"title" => "Matrix Completion: Old and New"
"description" => "KLOPP, O. (2019). Matrix Completion: Old and New. Dans: 2019 Structural Inference in High-Dimensional Models 2."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
]
"ouvrage" => "2019 Structural Inference in High-Dimensional Models 2"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => null
"en" => null
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
27 => Essec\Faculty\Model\Contribution {#2260
#_index: "academ_contributions"
#_id: "6736"
#_source: array:18 [
"id" => "6736"
"slug" => "network-models-and-sparse-graphon-estimation"
"yearMonth" => "2018-08"
"year" => "2018"
"title" => "Network Models and Sparse Graphon Estimation"
"description" => "KLOPP, O., TSYBAKOV, A. et VERZELEN, N. (2018). Network Models and Sparse Graphon Estimation. Dans: 14th Franco-Romanian Conference on Applied Mathematics."
"authors" => array:3 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "TSYBAKOV A."
]
2 => array:1 [
"name" => "VERZELEN N."
]
]
"ouvrage" => "14th Franco-Romanian Conference on Applied Mathematics"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => null
"en" => null
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
28 => Essec\Faculty\Model\Contribution {#2261
#_index: "academ_contributions"
#_id: "6815"
#_source: array:18 [
"id" => "6815"
"slug" => "optimal-graphon-estimation-in-cut-distance"
"yearMonth" => "2017-09"
"year" => "2017"
"title" => "Optimal Graphon Estimation in Cut Distance"
"description" => "KLOPP, O. et VERZELEN, N. (2017). Optimal Graphon Estimation in Cut Distance. Dans: Workshop on Community Detection and Network Reconstruction 2017."
"authors" => array:2 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "VERZELEN N."
]
]
"ouvrage" => "Workshop on Community Detection and Network Reconstruction 2017"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => null
"en" => null
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
29 => Essec\Faculty\Model\Contribution {#2262
#_index: "academ_contributions"
#_id: "6816"
#_source: array:18 [
"id" => "6816"
"slug" => "optimal-graphon-estimation-in-cut-distance"
"yearMonth" => "2018-06"
"year" => "2018"
"title" => "Optimal Graphon Estimation in Cut Distance"
"description" => "KLOPP, O. et VERZELEN, N. (2018). Optimal Graphon Estimation in Cut Distance. Dans: 27th Nordic Conference in Mathematical Statistics (NORDSTAT) 2018."
"authors" => array:2 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "VERZELEN N."
]
]
"ouvrage" => "27th Nordic Conference in Mathematical Statistics (NORDSTAT) 2018"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => null
"en" => null
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
30 => Essec\Faculty\Model\Contribution {#2263
#_index: "academ_contributions"
#_id: "6817"
#_source: array:18 [
"id" => "6817"
"slug" => "optimal-graphon-estimation-in-cut-distance"
"yearMonth" => "2018-08"
"year" => "2018"
"title" => "Optimal Graphon Estimation in Cut Distance"
"description" => "KLOPP, O. et VERZELEN, N. (2018). Optimal Graphon Estimation in Cut Distance. Dans: Tercera jornada Franco-Chilena de Estadística."
"authors" => array:2 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "VERZELEN N."
]
]
"ouvrage" => "Tercera jornada Franco-Chilena de Estadística"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => null
"en" => null
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
31 => Essec\Faculty\Model\Contribution {#2264
#_index: "academ_contributions"
#_id: "7153"
#_source: array:18 [
"id" => "7153"
"slug" => "sparse-network-estimation"
"yearMonth" => "2019-06"
"year" => "2019"
"title" => "Sparse Network Estimation"
"description" => "KLOPP, O. (2019). Sparse Network Estimation. Dans: 2019 The Power of Graphs in Machine Learning and Sequential Decision -making."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
]
"ouvrage" => "2019 The Power of Graphs in Machine Learning and Sequential Decision -making"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => null
"en" => null
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
32 => Essec\Faculty\Model\Contribution {#2265
#_index: "academ_contributions"
#_id: "7154"
#_source: array:18 [
"id" => "7154"
"slug" => "sparse-network-estimation"
"yearMonth" => "2019-07"
"year" => "2019"
"title" => "Sparse Network Estimation"
"description" => "KLOPP, O. (2019). Sparse Network Estimation. Dans: High dimensional probability and algorithms."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
]
"ouvrage" => "High dimensional probability and algorithms"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => null
"en" => null
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
33 => Essec\Faculty\Model\Contribution {#2266
#_index: "academ_contributions"
#_id: "7217"
#_source: array:18 [
"id" => "7217"
"slug" => "structured-matrix-estimation-and-completion"
"yearMonth" => "2018-06"
"year" => "2018"
"title" => "Structured Matrix Estimation and Completion"
"description" => "KLOPP, O., LU, Y., TSYBAKOV, A.B. et ZHOU, H.H. (2018). Structured Matrix Estimation and Completion. Dans: 4th Conference of the International Society for Nonparametric Statistics."
"authors" => array:4 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "LU Y."
]
2 => array:1 [
"name" => "TSYBAKOV A. B."
]
3 => array:1 [
"name" => "ZHOU H. H."
]
]
"ouvrage" => "4th Conference of the International Society for Nonparametric Statistics"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => null
"en" => null
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
34 => Essec\Faculty\Model\Contribution {#2267
#_index: "academ_contributions"
#_id: "3496"
#_source: array:18 [
"id" => "3496"
"slug" => "constructing-confidence-sets-for-the-matrix-completion-problem"
"yearMonth" => "2019-03"
"year" => "2019"
"title" => "Constructing Confidence Sets for the Matrix Completion Problem"
"description" => "CARPENTIER, A., KLOPP, O. et LÖFFLER, M. (2019). Constructing Confidence Sets for the Matrix Completion Problem. Dans: <i>Nonparametric Statistics</i>. 1st ed. Springer, pp. 103-119."
"authors" => array:3 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "CARPENTIER A."
]
2 => array:1 [
"name" => "LÖFFLER M."
]
]
"ouvrage" => "Nonparametric Statistics"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => "103-119"
"volume" => "250"
"number" => null
]
"type" => array:2 [
"fr" => "Chapitres"
"en" => "Book chapters"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "In the present chapter we consider the problem of constructing honest and adaptive confi- dence sets for the matrix completion problem. For the Bernoulli model with known variance of the noise we provide a realizable method for constructing confidence sets that adapt to the unknown rank of the true matrix."
"en" => "In the present chapter we consider the problem of constructing honest and adaptive confi- dence sets for the matrix completion problem. For the Bernoulli model with known variance of the noise we provide a realizable method for constructing confidence sets that adapt to the unknown rank of the true matrix."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
35 => Essec\Faculty\Model\Contribution {#2268
#_index: "academ_contributions"
#_id: "9407"
#_source: array:18 [
"id" => "9407"
"slug" => "hdr"
"yearMonth" => "2016-06"
"year" => "2016"
"title" => "HDR"
"description" => "KLOPP, O. (2016). HDR. France."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2020-12-17 18:37:46"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "HDR"
"en" => "HDR"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => null
"en" => null
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
36 => Essec\Faculty\Model\Contribution {#2269
#_index: "academ_contributions"
#_id: "14917"
#_source: array:18 [
"id" => "14917"
"slug" => "denoising-over-network-with-applications-to-partially-observed-epidemics"
"yearMonth" => "2024-06"
"year" => "2024"
"title" => "Denoising over network with applications to partially observed epidemics"
"description" => "KLOPP, O. (2024). Denoising over network with applications to partially observed epidemics. Dans: 2024 Statistical Machine Learning for High Dimensional Data. Singapore."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
]
"ouvrage" => "2024 Statistical Machine Learning for High Dimensional Data"
"keywords" => []
"updatedAt" => "2024-07-16 19:31:52"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => ""
"en" => ""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
37 => Essec\Faculty\Model\Contribution {#2270
#_index: "academ_contributions"
#_id: "14943"
#_source: array:18 [
"id" => "14943"
"slug" => "low-rank-density-estimation"
"yearMonth" => "2024-06"
"year" => "2024"
"title" => "Low-rank density estimation"
"description" => "KLOPP, O. (2024). Low-rank density estimation. Dans: 2024 Workshop on Heterogeneous and Distributed Data. Warwick."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
]
"ouvrage" => "2024 Workshop on Heterogeneous and Distributed Data"
"keywords" => []
"updatedAt" => "2024-07-19 13:21:08"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => ""
"en" => ""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
38 => Essec\Faculty\Model\Contribution {#2271
#_index: "academ_contributions"
#_id: "10431"
#_source: array:18 [
"id" => "10431"
"slug" => "rank-penalized-estimators-for-high-dimensional-matrices"
"yearMonth" => "2011-10"
"year" => "2011"
"title" => "Rank penalized estimators for high-dimensional matrices"
"description" => "KLOPP, O. (2011). Rank penalized estimators for high-dimensional matrices. <i>The Electronic Journal of Statistics</i>, 5, pp. 1161-1183."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-07-13 14:31:35"
"publicationUrl" => "https://projecteuclid.org/download/pdfview_1/euclid.ejs/1317906992"
"publicationInfo" => array:3 [
"pages" => "1161-1183"
"volume" => "5"
"number" => null
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
In this paper we consider the trace regression model. Assume\n
that we observe a small set of entries or linear combinations of entries of an\n
unknown matrix A0 corrupted by noise. We propose a new rank penalized\n
estimator of A0. For this estimator we establish general oracle inequality for\n
the prediction error both in probability and in expectation. We also prove\n
upper bounds for the rank of our estimator. Then, we apply our general\n
results to the problems of matrix completion and matrix regression. In\n
these cases our estimator has a particularly simple form: it is obtained by\n
hard thresholding of the singular values of a matrix constructed from the\n
observations.
"""
"en" => """
In this paper we consider the trace regression model. Assume\n
that we observe a small set of entries or linear combinations of entries of an\n
unknown matrix A0 corrupted by noise. We propose a new rank penalized\n
estimator of A0. For this estimator we establish general oracle inequality for\n
the prediction error both in probability and in expectation. We also prove\n
upper bounds for the rank of our estimator. Then, we apply our general\n
results to the problems of matrix completion and matrix regression. In\n
these cases our estimator has a particularly simple form: it is obtained by\n
hard thresholding of the singular values of a matrix constructed from the\n
observations.
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
39 => Essec\Faculty\Model\Contribution {#2272
#_index: "academ_contributions"
#_id: "10504"
#_source: array:18 [
"id" => "10504"
"slug" => "non-asymptotic-approach-to-varying-coefficient-model"
"yearMonth" => "2013-02"
"year" => "2013"
"title" => "Non-asymptotic approach to varying coefficient model"
"description" => "KLOPP, O. et PENSKY, M. (2013). Non-asymptotic approach to varying coefficient model. <i>The Electronic Journal of Statistics</i>, 7, pp. 454-479."
"authors" => array:2 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "PENSKY M."
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-07-13 14:31:37"
"publicationUrl" => "https://projecteuclid.org/download/pdfview_1/euclid.ejs/1360764852"
"publicationInfo" => array:3 [
"pages" => "454-479"
"volume" => "7"
"number" => null
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
In the present paper we consider the varying coefficient model\n
which represents a useful tool for exploring dynamic patterns in many applications.\n
Existing methods typically provide asymptotic evaluation of precision\n
of estimation procedures under the assumption that the number of\n
observations tends to infinity. In practical applications, however, only a fi-\n
nite number of measurements are available. In the present paper we focus on\n
a non-asymptotic approach to the problem. We propose a novel estimation\n
procedure which is based on recent developments in matrix estimation. In\n
particular, for our estimator, we obtain upper bounds for the mean squared\n
and the pointwise estimation errors. The obtained oracle inequalities are\n
non-asymptotic and hold for finite sample size
"""
"en" => """
In the present paper we consider the varying coefficient model\n
which represents a useful tool for exploring dynamic patterns in many applications.\n
Existing methods typically provide asymptotic evaluation of precision\n
of estimation procedures under the assumption that the number of\n
observations tends to infinity. In practical applications, however, only a fi-\n
nite number of measurements are available. In the present paper we focus on\n
a non-asymptotic approach to the problem. We propose a novel estimation\n
procedure which is based on recent developments in matrix estimation. In\n
particular, for our estimator, we obtain upper bounds for the mean squared\n
and the pointwise estimation errors. The obtained oracle inequalities are\n
non-asymptotic and hold for finite sample size
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
40 => Essec\Faculty\Model\Contribution {#2273
#_index: "academ_contributions"
#_id: "10560"
#_source: array:18 [
"id" => "10560"
"slug" => "noisy-low-rank-matrix-completion-with-general-sampling-distribution"
"yearMonth" => "2014-11"
"year" => "2014"
"title" => "Noisy low-rank matrix completion with general sampling distribution."
"description" => "KLOPP, O. (2014). Noisy low-rank matrix completion with general sampling distribution. <i>Bernoulli: A Journal of Mathematical Statistics and Probability</i>, 20(1), pp. 282-303."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-07-13 14:31:38"
"publicationUrl" => "https://projecteuclid.org/download/pdfview_1/euclid.bj/1390407290"
"publicationInfo" => array:3 [
"pages" => "282-303"
"volume" => "20"
"number" => "1"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
In the present paper, we consider the problem of matrix completion with noise. Unlike previous works,\n
we consider quite general sampling distribution and we do not need to know or to estimate the variance\n
of the noise. Two new nuclear-norm penalized estimators are proposed, one of them of “square-root” type.\n
We analyse their performance under high-dimensional scaling and provide non-asymptotic bounds on the\n
Frobenius norm error. Up to a logarithmic factor, these performance guarantees are minimax optimal in a\n
number of circumstances.
"""
"en" => """
In the present paper, we consider the problem of matrix completion with noise. Unlike previous works,\n
we consider quite general sampling distribution and we do not need to know or to estimate the variance\n
of the noise. Two new nuclear-norm penalized estimators are proposed, one of them of “square-root” type.\n
We analyse their performance under high-dimensional scaling and provide non-asymptotic bounds on the\n
Frobenius norm error. Up to a logarithmic factor, these performance guarantees are minimax optimal in a\n
number of circumstances.
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
41 => Essec\Faculty\Model\Contribution {#2274
#_index: "academ_contributions"
#_id: "10562"
#_source: array:18 [
"id" => "10562"
"slug" => "probabilistic-low-rank-matrix-completion-on-finite-alphabets"
"yearMonth" => "2014-12"
"year" => "2014"
"title" => "Probabilistic low-rank matrix completion on finite alphabets"
"description" => "KLOPP, O., LAFOND, J., MOULINES, E. et SALMON, J. (2014). Probabilistic low-rank matrix completion on finite alphabets. Dans: <i>NIPS</i>. Montréal: Neural Information Processing Systems."
"authors" => array:4 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "LAFOND J."
]
2 => array:1 [
"name" => "MOULINES E."
]
3 => array:1 [
"name" => "SALMON J."
]
]
"ouvrage" => "NIPS"
"keywords" => []
"updatedAt" => "2021-07-13 14:31:38"
"publicationUrl" => "http://papers.nips.cc/paper/5358-probabilistic-low-rank-matrix-completion-on-finite-alphabets"
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Actes d'une conférence"
"en" => "Conference Proceedings"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
The task of reconstructing a matrix given a sample of observed entries is known\n
as the matrix completion problem. It arises in a wide range of problems, including\n
recommender systems, collaborative filtering, dimensionality reduction,\n
image processing, quantum physics or multi-class classification to name a few.\n
Most works have focused on recovering an unknown real-valued low-rank matrix\n
from randomly sub-sampling its entries. Here, we investigate the case where\n
the observations take a finite number of values, corresponding for examples to\n
ratings in recommender systems or labels in multi-class classification. We also\n
consider a general sampling scheme (not necessarily uniform) over the matrix\n
entries. The performance of a nuclear-norm penalized estimator is analyzed theoretically.\n
More precisely, we derive bounds for the Kullback-Leibler divergence\n
between the true and estimated distributions. In practice, we have also proposed\n
an efficient algorithm based on lifted coordinate gradient descent in order to tackle\n
potentially high dimensional settings.
"""
"en" => """
The task of reconstructing a matrix given a sample of observed entries is known\n
as the matrix completion problem. It arises in a wide range of problems, including\n
recommender systems, collaborative filtering, dimensionality reduction,\n
image processing, quantum physics or multi-class classification to name a few.\n
Most works have focused on recovering an unknown real-valued low-rank matrix\n
from randomly sub-sampling its entries. Here, we investigate the case where\n
the observations take a finite number of values, corresponding for examples to\n
ratings in recommender systems or labels in multi-class classification. We also\n
consider a general sampling scheme (not necessarily uniform) over the matrix\n
entries. The performance of a nuclear-norm penalized estimator is analyzed theoretically.\n
More precisely, we derive bounds for the Kullback-Leibler divergence\n
between the true and estimated distributions. In practice, we have also proposed\n
an efficient algorithm based on lifted coordinate gradient descent in order to tackle\n
potentially high dimensional settings.
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
42 => Essec\Faculty\Model\Contribution {#2275
#_index: "academ_contributions"
#_id: "10574"
#_source: array:18 [
"id" => "10574"
"slug" => "adaptive-multinomial-matrix-completion"
"yearMonth" => "2015-01"
"year" => "2015"
"title" => "Adaptive Multinomial Matrix Completion"
"description" => "KLOPP, O., LAFOND, J., MOULINES, E. et SALMON, J. (2015). Adaptive Multinomial Matrix Completion. <i>The Electronic Journal of Statistics</i>, 9(2), pp. 2950-2975."
"authors" => array:4 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "LAFOND J."
]
2 => array:1 [
"name" => "MOULINES E."
]
3 => array:1 [
"name" => "SALMON J."
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-07-13 14:31:38"
"publicationUrl" => "https://projecteuclid.org/download/pdfview_1/euclid.ejs/1452004956"
"publicationInfo" => array:3 [
"pages" => "2950-2975"
"volume" => "9"
"number" => "2"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
The task of estimating a matrix given a sample of observed\n
entries is known as the matrix completion problem. Most works on matrix\n
completion have focused on recovering an unknown real-valued low-rank\n
matrix from a random sample of its entries. Here, we investigate the case\n
of highly quantized observations when the measurements can take only a\n
small number of values. These quantized outputs are generated according to\n
a probability distribution parametrized by the unknown matrix of interest.\n
This model corresponds, for example, to ratings in recommender systems\n
or labels in multi-class classification. We consider a general, non-uniform,\n
sampling scheme and give theoretical guarantees on the performance of a\n
constrained, nuclear norm penalized maximum likelihood estimator. One\n
important advantage of this estimator is that it does not require knowledge\n
of the rank or an upper bound on the nuclear norm of the unknown matrix\n
and, thus, it is adaptive. We provide lower bounds showing that our\n
estimator is minimax optimal. An efficient algorithm based on lifted coordinate\n
gradient descent is proposed to compute the estimator. A limited\n
Monte-Carlo experiment, using both simulated and real data is provided to\n
support our claims.
"""
"en" => """
The task of estimating a matrix given a sample of observed\n
entries is known as the matrix completion problem. Most works on matrix\n
completion have focused on recovering an unknown real-valued low-rank\n
matrix from a random sample of its entries. Here, we investigate the case\n
of highly quantized observations when the measurements can take only a\n
small number of values. These quantized outputs are generated according to\n
a probability distribution parametrized by the unknown matrix of interest.\n
This model corresponds, for example, to ratings in recommender systems\n
or labels in multi-class classification. We consider a general, non-uniform,\n
sampling scheme and give theoretical guarantees on the performance of a\n
constrained, nuclear norm penalized maximum likelihood estimator. One\n
important advantage of this estimator is that it does not require knowledge\n
of the rank or an upper bound on the nuclear norm of the unknown matrix\n
and, thus, it is adaptive. We provide lower bounds showing that our\n
estimator is minimax optimal. An efficient algorithm based on lifted coordinate\n
gradient descent is proposed to compute the estimator. A limited\n
Monte-Carlo experiment, using both simulated and real data is provided to\n
support our claims.
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
43 => Essec\Faculty\Model\Contribution {#2276
#_index: "academ_contributions"
#_id: "10586"
#_source: array:18 [
"id" => "10586"
"slug" => "estimation-of-matrices-with-row-sparsity"
"yearMonth" => "2015-10"
"year" => "2015"
"title" => "Estimation of matrices with row sparsity"
"description" => "KLOPP, O. et TSYBAKOV, A. (2015). Estimation of matrices with row sparsity. <i>Problems of Information Transmission</i>, 51(4), pp. 335-348."
"authors" => array:2 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "TSYBAKOV Alexandre"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-07-13 14:31:39"
"publicationUrl" => "https://hal.archives-ouvertes.fr/hal-01190696/document"
"publicationInfo" => array:3 [
"pages" => "335-348"
"volume" => "51"
"number" => "4"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "An increasing number of applications is concerned with recovering a sparse matrix from noisy observations. In this paper, we consider the setting where each row of an unknown matrix is sparse. We establish minimax optimal rates of convergence for estimating matrices with row sparsity. A major focus in the present paper is on the derivation of lower bounds."
"en" => "An increasing number of applications is concerned with recovering a sparse matrix from noisy observations. In this paper, we consider the setting where each row of an unknown matrix is sparse. We establish minimax optimal rates of convergence for estimating matrices with row sparsity. A major focus in the present paper is on the derivation of lower bounds."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
44 => Essec\Faculty\Model\Contribution {#2277
#_index: "academ_contributions"
#_id: "10603"
#_source: array:18 [
"id" => "10603"
"slug" => "matrix-completion-by-singular-value-thresholding-sharp-bounds"
"yearMonth" => "2015-01"
"year" => "2015"
"title" => "Matrix completion by singular value thresholding : sharp bounds"
"description" => "KLOPP, O. (2015). Matrix completion by singular value thresholding : sharp bounds. <i>The Electronic Journal of Statistics</i>, 9(2), pp. 2348-2369."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-07-13 14:31:39"
"publicationUrl" => "https://projecteuclid.org/download/pdfview_1/euclid.ejs/1445605702"
"publicationInfo" => array:3 [
"pages" => "2348-2369"
"volume" => "9"
"number" => "2"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
We consider the matrix completion problem where the aim is toestimate a large data matrix for which only a relatively small random subset\n
of its entries is observed. Quite popular approaches to matrix completion\n
problem are iterative thresholding methods. In spite of their empirical success,\n
the theoretical guarantees of such iterative thresholding methods are\n
poorly understood. The goal of this paper is to provide strong theoretical\n
guarantees, similar to those obtained for nuclear-norm penalization methods\n
and one step thresholding methods, for an iterative thresholding algorithm\n
which is a modification of the softImpute algorithm. An important\n
consequence of our result is the exact minimax optimal rates of convergence\n
for matrix completion problem which were know until now only up\n
to a logarithmic factor.
"""
"en" => """
We consider the matrix completion problem where the aim is to\n
estimate a large data matrix for which only a relatively small random subset\n
of its entries is observed. Quite popular approaches to matrix completion\n
problem are iterative thresholding methods. In spite of their empirical success,\n
the theoretical guarantees of such iterative thresholding methods are\n
poorly understood. The goal of this paper is to provide strong theoretical\n
guarantees, similar to those obtained for nuclear-norm penalization methods\n
and one step thresholding methods, for an iterative thresholding algorithm\n
which is a modification of the softImpute algorithm. An important\n
consequence of our result is the exact minimax optimal rates of convergence\n
for matrix completion problem which were know until now only up\n
to a logarithmic factor.
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
45 => Essec\Faculty\Model\Contribution {#2278
#_index: "academ_contributions"
#_id: "10610"
#_source: array:18 [
"id" => "10610"
"slug" => "robust-matrix-completion"
"yearMonth" => "2015-07"
"year" => "2015"
"title" => "Robust Matrix Completion"
"description" => "KLOPP, O. (2015). Robust Matrix Completion. Dans: ISNPS Biosciences, Medicine, and novel Non-parametric Methods. Graz."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
]
"ouvrage" => "ISNPS Biosciences, Medicine, and novel Non-parametric Methods"
"keywords" => []
"updatedAt" => "2021-07-13 14:31:39"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
This paper considers the problem of estimation of a low-rank matrix\n
when most of its entries are not observed and some of the observed entries\n
are corrupted. The observations are noisy realizations of a sum of a\n
low-rank matrix, which we wish to estimate, and a second matrix having\n
a complementary sparse structure such as elementwise sparsity or columnwise\n
sparsity. We analyze a class of estimators obtained as solutions of\n
a constrained convex optimization problem combining the nuclear norm\n
penalty and a convex relaxation penalty for the sparse constraint. Our\n
assumptions allow for simultaneous presence of random and deterministic\n
patterns in the sampling scheme. We establish rates of convergence for\n
the low-rank component from partial and corrupted observations in the\n
presence of noise and we show that these rates are minimax optimal up\n
to logarithmic factors.
"""
"en" => """
This paper considers the problem of estimation of a low-rank matrix\n
when most of its entries are not observed and some of the observed entries\n
are corrupted. The observations are noisy realizations of a sum of a\n
low-rank matrix, which we wish to estimate, and a second matrix having\n
a complementary sparse structure such as elementwise sparsity or columnwise\n
sparsity. We analyze a class of estimators obtained as solutions of\n
a constrained convex optimization problem combining the nuclear norm\n
penalty and a convex relaxation penalty for the sparse constraint. Our\n
assumptions allow for simultaneous presence of random and deterministic\n
patterns in the sampling scheme. We establish rates of convergence for\n
the low-rank component from partial and corrupted observations in the\n
presence of noise and we show that these rates are minimax optimal up\n
to logarithmic factors.
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
46 => Essec\Faculty\Model\Contribution {#2279
#_index: "academ_contributions"
#_id: "10611"
#_source: array:18 [
"id" => "10611"
"slug" => "robust-matrix-completion"
"yearMonth" => "2015-06"
"year" => "2015"
"title" => "Robust Matrix Completion"
"description" => "KLOPP, O. (2015). Robust Matrix Completion. Dans: The First International Conference on missing values MissData. Rennes."
"authors" => array:1 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
]
"ouvrage" => "The First International Conference on missing values MissData"
"keywords" => []
"updatedAt" => "2021-07-13 14:31:39"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
This paper considers the problem of estimation of a low-rank matrix\n
when most of its entries are not observed and some of the observed entries\n
are corrupted. The observations are noisy realizations of a sum of a\n
low-rank matrix, which we wish to estimate, and a second matrix having\n
a complementary sparse structure such as elementwise sparsity or columnwise\n
sparsity. We analyze a class of estimators obtained as solutions of\n
a constrained convex optimization problem combining the nuclear norm\n
penalty and a convex relaxation penalty for the sparse constraint. Our\n
assumptions allow for simultaneous presence of random and deterministic\n
patterns in the sampling scheme. We establish rates of convergence for\n
the low-rank component from partial and corrupted observations in the\n
presence of noise and we show that these rates are minimax optimal up\n
to logarithmic factors.
"""
"en" => """
This paper considers the problem of estimation of a low-rank matrix\n
when most of its entries are not observed and some of the observed entries\n
are corrupted. The observations are noisy realizations of a sum of a\n
low-rank matrix, which we wish to estimate, and a second matrix having\n
a complementary sparse structure such as elementwise sparsity or columnwise\n
sparsity. We analyze a class of estimators obtained as solutions of\n
a constrained convex optimization problem combining the nuclear norm\n
penalty and a convex relaxation penalty for the sparse constraint. Our\n
assumptions allow for simultaneous presence of random and deterministic\n
patterns in the sampling scheme. We establish rates of convergence for\n
the low-rank component from partial and corrupted observations in the\n
presence of noise and we show that these rates are minimax optimal up\n
to logarithmic factors.
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
47 => Essec\Faculty\Model\Contribution {#2280
#_index: "academ_contributions"
#_id: "10613"
#_source: array:18 [
"id" => "10613"
"slug" => "sparse-high-dimensional-varying-coefficient-model-non-asymptotic-minimax-study"
"yearMonth" => "2015-05"
"year" => "2015"
"title" => "Sparse high-dimensional varying coefficient model : non-asymptotic minimax study."
"description" => "KLOPP, O. et PENSKY, M. (2015). Sparse high-dimensional varying coefficient model : non-asymptotic minimax study. <i>Annals of Statistics</i>, 43(3), pp. 1273-1299."
"authors" => array:2 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "PENSKY M."
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-07-13 14:31:39"
"publicationUrl" => "https://arxiv.org/pdf/1312.4087v2.pdf"
"publicationInfo" => array:3 [
"pages" => "1273-1299"
"volume" => "43"
"number" => "3"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
The objective of the present paper is to develop a minimax theory for the varying coefficient\n
model in a non-asymptotic setting. We consider a high-dimensional sparse varying\n
coefficient model where only few of the covariates are present and only some of those covariates\n
are time dependent. Our analysis allows the time dependent covariates to have different\n
degrees of smoothness and to be spatially inhomogeneous. We develop the minimax lower\n
bounds for the quadratic risk and construct an adaptive estimator which attains those lower\n
bounds within a constant (if all time-dependent covariates are spatially homogeneous) or\n
logarithmic factor of the number of observations.
"""
"en" => """
The objective of the present paper is to develop a minimax theory for the varying coefficient\n
model in a non-asymptotic setting. We consider a high-dimensional sparse varying\n
coefficient model where only few of the covariates are present and only some of those covariates\n
are time dependent. Our analysis allows the time dependent covariates to have different\n
degrees of smoothness and to be spatially inhomogeneous. We develop the minimax lower\n
bounds for the quadratic risk and construct an adaptive estimator which attains those lower\n
bounds within a constant (if all time-dependent covariates are spatially homogeneous) or\n
logarithmic factor of the number of observations.
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
48 => Essec\Faculty\Model\Contribution {#2281
#_index: "academ_contributions"
#_id: "10672"
#_source: array:18 [
"id" => "10672"
"slug" => "high-dimensional-matrix-estimation-with-unknown-variance-of-the-noise"
"yearMonth" => "2017-01"
"year" => "2017"
"title" => "High dimensional matrix estimation with unknown variance of the noise"
"description" => "KLOPP, O. et GAIFFAS, S. (2017). High dimensional matrix estimation with unknown variance of the noise. <i>Statistica Sinica</i>, 27(1), pp. 115-145."
"authors" => array:2 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "GAIFFAS Stefano"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://hal.archives-ouvertes.fr/hal-00649437v4/document"
"publicationInfo" => array:3 [
"pages" => "115-145"
"volume" => "27"
"number" => "1"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
Assume that we observe a small set of entries or linear combinations of entries\n
of an unknown matrix A corrupted by noise. We propose a new method for\n
estimating A which does not rely on the knowledge or on an estimation of the\n
standard deviation of the noise s. Our estimator achieves, up to a logarithmic\n
factor, optimal rates of convergence under the Frobenius risk and, thus, has the\n
same prediction performance as previously proposed estimators which rely on the\n
knowledge of s. Some numerical experiments show the benefits of this approach.
"""
"en" => """
Assume that we observe a small set of entries or linear combinations of entries\n
of an unknown matrix A corrupted by noise. We propose a new method for\n
estimating A which does not rely on the knowledge or on an estimation of the\n
standard deviation of the noise s. Our estimator achieves, up to a logarithmic\n
factor, optimal rates of convergence under the Frobenius risk and, thus, has the\n
same prediction performance as previously proposed estimators which rely on the\n
knowledge of s. Some numerical experiments show the benefits of this approach.
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
49 => Essec\Faculty\Model\Contribution {#2282
#_index: "academ_contributions"
#_id: "10678"
#_source: array:18 [
"id" => "10678"
"slug" => "oracle-inequalities-for-network-models-and-sparse-graphon-estimation"
"yearMonth" => "2017-02"
"year" => "2017"
"title" => "Oracle inequalities for network models and sparse graphon estimation"
"description" => "KLOPP, O., TSYBAKOV, A. et VERZELEN, N. (2017). Oracle inequalities for network models and sparse graphon estimation. <i>Annals of Statistics</i>, 45(1), pp. 316-354."
"authors" => array:3 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "TSYBAKOV Alexandre"
]
2 => array:1 [
"name" => "VERZELEN Nicolas"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://hal.archives-ouvertes.fr/hal-01176210/document"
"publicationInfo" => array:3 [
"pages" => "316-354"
"volume" => "45"
"number" => "1"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
Inhomogeneous random graph models encompass many network models such as stochastic block\n
models and latent position models. We consider the problem of statistical estimation of the matrix of\n
connection probabilities based on the observations of the adjacency matrix of the network. Taking the\n
stochastic block model as an approximation, we construct estimators of network connection probabilities\n
– the ordinary block constant least squares estimator, and its restricted version. We show that they\n
satisfy oracle inequalities with respect to the block constant oracle. As a consequence, we derive optimal\n
rates of estimation of the probability matrix. Our results cover the important setting of sparse networks.\n
Another consequence consists in establishing upper bounds on the minimax risks for graphon estimation\n
in the L2 norm when the probability matrix is sampled according to a graphon model. These bounds\n
include an additional term accounting for the “agnostic” error induced by the variability of the latent\n
unobserved variables of the graphon model. In this setting, the optimal rates are influenced not only\n
by the bias and variance components as in usual nonparametric problems but also include the third\n
component, which is the agnostic error. The results shed light on the differences between estimation\n
under the empirical loss (the probability matrix estimation) and under the integrated loss (the graphon\n
estimation).
"""
"en" => """
Inhomogeneous random graph models encompass many network models such as stochastic block\n
models and latent position models. We consider the problem of statistical estimation of the matrix of\n
connection probabilities based on the observations of the adjacency matrix of the network. Taking the\n
stochastic block model as an approximation, we construct estimators of network connection probabilities\n
– the ordinary block constant least squares estimator, and its restricted version. We show that they\n
satisfy oracle inequalities with respect to the block constant oracle. As a consequence, we derive optimal\n
rates of estimation of the probability matrix. Our results cover the important setting of sparse networks.\n
Another consequence consists in establishing upper bounds on the minimax risks for graphon estimation\n
in the L2 norm when the probability matrix is sampled according to a graphon model. These bounds\n
include an additional term accounting for the “agnostic” error induced by the variability of the latent\n
unobserved variables of the graphon model. In this setting, the optimal rates are influenced not only\n
by the bias and variance components as in usual nonparametric problems but also include the third\n
component, which is the agnostic error. The results shed light on the differences between estimation\n
under the empirical loss (the probability matrix estimation) and under the integrated loss (the graphon\n
estimation).
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
50 => Essec\Faculty\Model\Contribution {#2283
#_index: "academ_contributions"
#_id: "10681"
#_source: array:18 [
"id" => "10681"
"slug" => "robust-matrix-completion"
"yearMonth" => "2017-01"
"year" => "2017"
"title" => "Robust Matrix Completion"
"description" => "KLOPP, O., LOUNICI, K. et TSYBAKOV, A. (2017). Robust Matrix Completion. <i>Probability Theory and Related Fields</i>, 169(43862), pp. 523-564."
"authors" => array:3 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "LOUNICI K."
]
2 => array:1 [
"name" => "TSYBAKOV Alexandre"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://hal.archives-ouvertes.fr/hal-01098492/document"
"publicationInfo" => array:3 [
"pages" => "523-564"
"volume" => "169"
"number" => "43862"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
This paper considers the problem of estimation of a low-rank matrix\n
when most of its entries are not observed and some of the observed entries\n
are corrupted. The observations are noisy realizations of a sum of a\n
low-rank matrix, which we wish to estimate, and a second matrix having\n
a complementary sparse structure such as elementwise sparsity or columnwise\n
sparsity. We analyze a class of estimators obtained as solutions of\n
a constrained convex optimization problem combining the nuclear norm\n
penalty and a convex relaxation penalty for the sparse constraint. Our\n
assumptions allow for simultaneous presence of random and deterministic\n
patterns in the sampling scheme. We establish rates of convergence for\n
the low-rank component from partial and corrupted observations in the\n
presence of noise and we show that these rates are minimax optimal up\n
to logarithmic factors.
"""
"en" => """
This paper considers the problem of estimation of a low-rank matrix\n
when most of its entries are not observed and some of the observed entries\n
are corrupted. The observations are noisy realizations of a sum of a\n
low-rank matrix, which we wish to estimate, and a second matrix having\n
a complementary sparse structure such as elementwise sparsity or columnwise\n
sparsity. We analyze a class of estimators obtained as solutions of\n
a constrained convex optimization problem combining the nuclear norm\n
penalty and a convex relaxation penalty for the sparse constraint. Our\n
assumptions allow for simultaneous presence of random and deterministic\n
patterns in the sampling scheme. We establish rates of convergence for\n
the low-rank component from partial and corrupted observations in the\n
presence of noise and we show that these rates are minimax optimal up\n
to logarithmic factors.
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
51 => Essec\Faculty\Model\Contribution {#2284
#_index: "academ_contributions"
#_id: "10686"
#_source: array:18 [
"id" => "10686"
"slug" => "adaptive-confidence-sets-for-matrix-completion"
"yearMonth" => "2018-03"
"year" => "2018"
"title" => "Adaptive confidence sets for matrix completion"
"description" => "KLOPP, O., CARPENTIER, A., LÖFFLER, M. et NICKL, R. (2018). Adaptive confidence sets for matrix completion. <i>Bernoulli: A Journal of Mathematical Statistics and Probability</i>, 24(4A), pp. 2429-2460."
"authors" => array:4 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "CARPENTIER Alexandra"
]
2 => array:1 [
"name" => "LÖFFLER Mattias"
]
3 => array:1 [
"name" => "NICKL R."
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://projecteuclid.org/euclid.bj/1522051214"
"publicationInfo" => array:3 [
"pages" => "2429-2460"
"volume" => "24"
"number" => "4A"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "In the present paper, we study the problem of existence of honest and adaptive confidence sets for matrix completion. We consider two statistical models: the trace regression model and the Bernoulli model. In the trace regression model, we show that honest confidence sets that adapt to the unknown rank of the matrix exist even when the error variance is unknown. Contrary to this, we prove that in the Bernoulli model, honest and adaptive confidence sets exist only when the error variance is known a priori. In the course of our proofs, we obtain bounds for the minimax rates of certain composite hypothesis testing problems arising in low rank inference."
"en" => "In the present paper, we study the problem of existence of honest and adaptive confidence sets for matrix completion. We consider two statistical models: the trace regression model and the Bernoulli model. In the trace regression model, we show that honest confidence sets that adapt to the unknown rank of the matrix exist even when the error variance is unknown. Contrary to this, we prove that in the Bernoulli model, honest and adaptive confidence sets exist only when the error variance is known a priori. In the course of our proofs, we obtain bounds for the minimax rates of certain composite hypothesis testing problems arising in low rank inference."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
52 => Essec\Faculty\Model\Contribution {#2285
#_index: "academ_contributions"
#_id: "10689"
#_source: array:18 [
"id" => "10689"
"slug" => "network-models-and-sparse-graphon-estimation"
"yearMonth" => "2018-06"
"year" => "2018"
"title" => "Network models and sparse graphon estimation."
"description" => "KLOPP, O., TSYBAKOV, A. et VERZELEN, N. (2018). Network models and sparse graphon estimation. Dans: NordStat 2018. Tartu."
"authors" => array:3 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "TSYBAKOV Alexandre"
]
2 => array:1 [
"name" => "VERZELEN Nicolas"
]
]
"ouvrage" => "NordStat 2018"
"keywords" => array:6 [
0 => "inhomogeneous random graph"
1 => "networks"
2 => "oracle inequality"
3 => """
sparse\n
graphon
"""
4 => "sparsity"
5 => "stochastic block model"
]
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
Inhomogeneous random graph models encompass many network models such as\n
stochastic block models and latent position models. We consider the problem of\n
statistical estimation of the matrix of connection probabilities based on the obser-\n
vations of the adjacency matrix of the network. Taking the stochastic block model\n
as an approximation, we construct estimators of network connection probabilities –\n
the ordinary block constant least squares estimator, and its restricted version. We\n
show that they satisfy oracle inequalities with respect to the block constant oracle.\n
As a consequence, we derive optimal rates of estimation of the probability matrix.\n
Our results cover the important setting of sparse networks. Another consequence\n
consists in establishing upper bounds on the minimax risks for graphon estimation\n
in the L 2 norm when the probability matrix is sampled according to a graphon\n
model. These bounds include an additional term accounting for the “agnostic”\n
error induced by the variability of the latent unobserved variables of the graphon\n
model. In this setting, the optimal rates are influenced not only by the bias and\n
variance components as in usual nonparametric problems but also include the third\n
component, which is the agnostic error. The results shed light on the differences\n
between estimation under the empirical loss (the probability matrix estimation) and\n
under the integrated loss (the graphon estimation).
"""
"en" => """
Inhomogeneous random graph models encompass many network models such as\n
stochastic block models and latent position models. We consider the problem of\n
statistical estimation of the matrix of connection probabilities based on the obser-\n
vations of the adjacency matrix of the network. Taking the stochastic block model\n
as an approximation, we construct estimators of network connection probabilities –\n
the ordinary block constant least squares estimator, and its restricted version. We\n
show that they satisfy oracle inequalities with respect to the block constant oracle.\n
As a consequence, we derive optimal rates of estimation of the probability matrix.\n
Our results cover the important setting of sparse networks. Another consequence\n
consists in establishing upper bounds on the minimax risks for graphon estimation\n
in the L 2 norm when the probability matrix is sampled according to a graphon\n
model. These bounds include an additional term accounting for the “agnostic”\n
error induced by the variability of the latent unobserved variables of the graphon\n
model. In this setting, the optimal rates are influenced not only by the bias and\n
variance components as in usual nonparametric problems but also include the third\n
component, which is the agnostic error. The results shed light on the differences\n
between estimation under the empirical loss (the probability matrix estimation) and\n
under the integrated loss (the graphon estimation).
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
53 => Essec\Faculty\Model\Contribution {#2286
#_index: "academ_contributions"
#_id: "10692"
#_source: array:18 [
"id" => "10692"
"slug" => "variety-and-veracity-of-the-data-in-matrix-completion"
"yearMonth" => "2018-06"
"year" => "2018"
"title" => "Variety and Veracity of the Data in Matrix Completion"
"description" => "KLOPP, O., LOUNICI, K., TSYBAKOV, A. et ALAYA, M. (2018). Variety and Veracity of the Data in Matrix Completion. Dans: The 40th Conference on Stochastic Processes and their Applications. Gothenburg."
"authors" => array:4 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "LOUNICI Karim"
]
2 => array:1 [
"name" => "TSYBAKOV Alexandre"
]
3 => array:1 [
"name" => "ALAYA Mokhtar"
]
]
"ouvrage" => "The 40th Conference on Stochastic Processes and their Applications"
"keywords" => array:4 [
0 => "high-dimensional prediction"
1 => "matrix completion"
2 => "low-rank matrix estimation"
3 => """
robust\n
estimation
"""
]
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Communications dans une conférence"
"en" => "Presentations at an Academic or Professional conference"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
Beyond volume, variety and veracity are two important issues of the modern data. In this talk we discuss\n
these questions in the context of the matrix completion problem. First, we considers the problem of estimation of\n
a low-rank matrix when most of its entries are not observed and some of the observed entries are corrupted. The\n
observations are noisy realizations of a sum of a low-rank matrix, which we wish to estimate, and a second matrix\n
having a complementary sparse structure such as elementwise sparsity or columnwise sparsity. We analyze a class of\n
estimators obtained as solutions of a constrained convex optimization problem combining the nuclear norm penalty\n
and a convex relaxation penalty for the sparse constraint.\n
In practical situations, data is often obtained from multiple sources which results in a collection of matrices\n
rather a single one. In the second part, we consider the problem of collective matrix completion with multiple and\n
heterogeneous matrices, which can be count, binary, continuous, etc. We first investigate the setting where, for each\n
source, the matrix entries are sampled from an exponential family distribution. Then we deal with the distribution-\n
free setting. The estimation procedures are based on the penalized nuclear norm estimators. We prove that the\n
proposed estimators achieve fast rates of convergence under the two considered setting.
"""
"en" => """
Beyond volume, variety and veracity are two important issues of the modern data. In this talk we discuss\n
these questions in the context of the matrix completion problem. First, we considers the problem of estimation of\n
a low-rank matrix when most of its entries are not observed and some of the observed entries are corrupted. The\n
observations are noisy realizations of a sum of a low-rank matrix, which we wish to estimate, and a second matrix\n
having a complementary sparse structure such as elementwise sparsity or columnwise sparsity. We analyze a class of\n
estimators obtained as solutions of a constrained convex optimization problem combining the nuclear norm penalty\n
and a convex relaxation penalty for the sparse constraint.\n
In practical situations, data is often obtained from multiple sources which results in a collection of matrices\n
rather a single one. In the second part, we consider the problem of collective matrix completion with multiple and\n
heterogeneous matrices, which can be count, binary, continuous, etc. We first investigate the setting where, for each\n
source, the matrix entries are sampled from an exponential family distribution. Then we deal with the distribution-\n
free setting. The estimation procedures are based on the penalized nuclear norm estimators. We prove that the\n
proposed estimators achieve fast rates of convergence under the two considered setting.
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
54 => Essec\Faculty\Model\Contribution {#2287
#_index: "academ_contributions"
#_id: "10732"
#_source: array:18 [
"id" => "10732"
"slug" => "structured-matrix-estimation-and-completion"
"yearMonth" => "2019-10"
"year" => "2019"
"title" => "Structured Matrix Estimation and Completion"
"description" => "KLOPP, O., LU, Y., TSYBAKOV, A.B. et ZHOU, H.H. (2019). Structured Matrix Estimation and Completion. <i>Bernoulli: A Journal of Mathematical Statistics and Probability</i>, 4B(25), pp. 3883-3911."
"authors" => array:4 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "LU Y."
]
2 => array:1 [
"name" => "TSYBAKOV A. B."
]
3 => array:1 [
"name" => "ZHOU H. H."
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://www.researchgate.net/publication/318316260_Structured_Matrix_Estimation_and_Completion"
"publicationInfo" => array:3 [
"pages" => "3883-3911"
"volume" => "4B"
"number" => "25"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "We study the problem of matrix estimation and matrix completion under a general framework. This framework includes several important models as special cases such as the gaussian mixture model, mixed membership model, bi-clustering model and dictionary learning. We consider the optimal convergence rates in a minimax sense for estimation of the signal matrix under the Frobenius norm and under the spectral norm. As a consequence of our general result we obtain minimax optimal rates of convergence for various special models."
"en" => "We study the problem of matrix estimation and matrix completion under a general framework. This framework includes several important models as special cases such as the gaussian mixture model, mixed membership model, bi-clustering model and dictionary learning. We consider the optimal convergence rates in a minimax sense for estimation of the signal matrix under the Frobenius norm and under the spectral norm. As a consequence of our general result we obtain minimax optimal rates of convergence for various special models."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
55 => Essec\Faculty\Model\Contribution {#2288
#_index: "academ_contributions"
#_id: "12662"
#_source: array:18 [
"id" => "12662"
"slug" => "outlier-detection-in-networks-with-missing-links"
"yearMonth" => "2021-12"
"year" => "2021"
"title" => "Outlier detection in networks with missing links"
"description" => "GAUCHER, S., KLOPP, O. et ROBIN, G. (2021). Outlier detection in networks with missing links. <i>Computational Statistics and Data Analysis</i>, 164, pp. 107308."
"authors" => array:3 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "GAUCHER Solenne"
]
2 => array:1 [
"name" => "ROBIN Geneviève"
]
]
"ouvrage" => ""
"keywords" => array:4 [
0 => "Outlier detection"
1 => "Robust network estimation"
2 => "Missing observations"
3 => "Link prediction"
]
"updatedAt" => "2023-01-27 01:00:40"
"publicationUrl" => "https://www.sciencedirect.com/science/article/pii/S0167947321001420?via%3Dihub"
"publicationInfo" => array:3 [
"pages" => "107308"
"volume" => "164"
"number" => ""
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "Outliers arise in networks due to different reasons such as fraudulent behaviour of malicious users or default in measurement instruments and can significantly impair network analyses. In addition, real-life networks are likely to be incompletely observed, with missing links due to individual non-response or machine failures. Therefore, identifying outliers in the presence of missing links is a crucial problem in network analysis. A new algorithm is introduced to detect outliers in a network and simultaneously predict the missing links. The proposed method is statistically sound: under fairly general assumptions, this algorithm exactly detects the outliers, and achieves the best known error for the prediction of missing links with polynomial computational cost."
"en" => "Outliers arise in networks due to different reasons such as fraudulent behaviour of malicious users or default in measurement instruments and can significantly impair network analyses. In addition, real-life networks are likely to be incompletely observed, with missing links due to individual non-response or machine failures. Therefore, identifying outliers in the presence of missing links is a crucial problem in network analysis. A new algorithm is introduced to detect outliers in a network and simultaneously predict the missing links. The proposed method is statistically sound: under fairly general assumptions, this algorithm exactly detects the outliers, and achieves the best known error for the prediction of missing links with polynomial computational cost."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
56 => Essec\Faculty\Model\Contribution {#2289
#_index: "academ_contributions"
#_id: "12663"
#_source: array:18 [
"id" => "12663"
"slug" => "maximum-likelihood-estimation-of-sparse-networks-with-missing-observations"
"yearMonth" => "2021-12"
"year" => "2021"
"title" => "Maximum likelihood estimation of sparse networks with missing observations"
"description" => "GAUCHER, S. et KLOPP, O. (2021). Maximum likelihood estimation of sparse networks with missing observations. <i>Journal of Statistical Planning and Inference</i>, 215, pp. 299-329."
"authors" => array:2 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "GAUCHER Solenne"
]
]
"ouvrage" => ""
"keywords" => array:5 [
0 => "Missing observations"
1 => "Network models"
2 => "Sparse estimation"
3 => "Graphon model"
4 => "Variational approximation"
]
"updatedAt" => "2023-01-27 01:00:40"
"publicationUrl" => "https://www.sciencedirect.com/science/article/pii/S0378375821000422#!"
"publicationInfo" => array:3 [
"pages" => "299-329"
"volume" => "215"
"number" => ""
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "Estimating the matrix of connections probabilities is one of the key questions when studying sparse networks. In this work, we consider networks generated under the sparse graphon model and the inhomogeneous random graph model with missing observations. Using the Stochastic Block Model as a parametric proxy, we bound the risk of the maximum likelihood estimator of network connections probabilities, and show that it is minimax optimal. Moreover, we show that our estimator can be efficiently approximated using tractable variational methods, and thus used in practice."
"en" => "Estimating the matrix of connections probabilities is one of the key questions when studying sparse networks. In this work, we consider networks generated under the sparse graphon model and the inhomogeneous random graph model with missing observations. Using the Stochastic Block Model as a parametric proxy, we bound the risk of the maximum likelihood estimator of network connections probabilities, and show that it is minimax optimal. Moreover, we show that our estimator can be efficiently approximated using tractable variational methods, and thus used in practice."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T08:21:48.000Z"
]
+lang: "en"
+"_type": "_doc"
+"_score": 5.298641
+"parent": null
}
57 => Essec\Faculty\Model\Contribution {#2290
#_index: "academ_contributions"
#_id: "14076"
#_source: array:18 [
"id" => "14076"
"slug" => "optimality-of-variational-inference-for-stochastic-block-model-with-missing-links"
"yearMonth" => "2021-12"
"year" => "2021"
"title" => "Optimality of variational inference for stochastic block model with missing links"
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58 => Essec\Faculty\Model\Contribution {#2291
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2 => array:1 [
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59 => Essec\Faculty\Model\Contribution {#2292
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60 => Essec\Faculty\Model\Contribution {#2293
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