Essec\Faculty\Model\Profile {#2233
#_id: "B00760223"
#_source: array:40 [
"bid" => "B00760223"
"academId" => "1964"
"slug" => "heng-jeremy"
"fullName" => "Jeremy HENG"
"lastName" => "HENG"
"firstName" => "Jeremy"
"title" => array:2 [
"fr" => "Professeur associé"
"en" => "Associate Professor"
]
"email" => "heng@essec.edu"
"status" => "ACTIF"
"campus" => "Campus de Singapour"
"departments" => []
"phone" => "+65 6413 9753"
"sites" => []
"facNumber" => "1964"
"externalCvUrl" => "https://faculty.essec.edu/cv/heng-jeremy/pdf"
"googleScholarUrl" => "https://scholar.google.com/citations?user=XzGQ0CgAAAAJ"
"facOrcId" => "https://orcid.org/0000-0003-4959-6856"
"career" => array:3 [
0 => Essec\Faculty\Model\CareerItem {#2238
#_index: null
#_id: null
#_source: array:7 [
"startDate" => "2019-01-01"
"endDate" => "2024-08-31"
"isInternalPosition" => true
"type" => array:2 [
"fr" => "Positions académiques principales"
"en" => "Full-time academic appointments"
]
"label" => array:2 [
"fr" => "Professeur assistant"
"en" => "Assistant Professor"
]
"institution" => array:2 [
"fr" => "ESSEC Business School"
"en" => "ESSEC Business School"
]
"country" => array:2 [
"fr" => "Singapour"
"en" => "Singapore"
]
]
+lang: "fr"
+"parent": Essec\Faculty\Model\Profile {#2233}
}
1 => Essec\Faculty\Model\CareerItem {#2232
#_index: null
#_id: null
#_source: array:7 [
"startDate" => "2017-09-01"
"endDate" => "2019-07-31"
"isInternalPosition" => true
"type" => array:2 [
"en" => "Other Academic Appointments"
"fr" => "Autres positions académiques"
]
"label" => array:2 [
"fr" => "Postdoctoral Fellow"
"en" => "Postdoctoral Fellow"
]
"institution" => array:2 [
"fr" => "Harvard University"
"en" => "Harvard University"
]
"country" => array:2 [
"fr" => "États-Unis"
"en" => "United States of America"
]
]
+lang: "fr"
+"parent": Essec\Faculty\Model\Profile {#2233}
}
2 => Essec\Faculty\Model\CareerItem {#2236
#_index: null
#_id: null
#_source: array:7 [
"startDate" => "2024-09-01"
"endDate" => null
"isInternalPosition" => true
"type" => array:2 [
"fr" => "Positions académiques principales"
"en" => "Full-time academic appointments"
]
"label" => array:2 [
"fr" => "Professeur associé"
"en" => "Associate Professor"
]
"institution" => array:2 [
"fr" => "ESSEC Business School"
"en" => "ESSEC Business School"
]
"country" => array:2 [
"fr" => "France"
"en" => "France"
]
]
+lang: "fr"
+"parent": Essec\Faculty\Model\Profile {#2233}
}
]
"diplomes" => array:2 [
0 => Essec\Faculty\Model\Diplome {#2235
#_index: null
#_id: null
#_source: array:6 [
"diplome" => "DIPLOMA"
"type" => array:2 [
"fr" => "Diplômes"
"en" => "Diplomas"
]
"year" => "2017"
"label" => array:2 [
"en" => "PhD in Statistics"
"fr" => "PhD en Statistiques"
]
"institution" => array:2 [
"fr" => "University of Oxford"
"en" => "University of Oxford"
]
"country" => array:2 [
"fr" => "Royaume-Uni"
"en" => "United Kingdom"
]
]
+lang: "fr"
+"parent": Essec\Faculty\Model\Profile {#2233}
}
1 => Essec\Faculty\Model\Diplome {#2237
#_index: null
#_id: null
#_source: array:6 [
"diplome" => "DIPLOMA"
"type" => array:2 [
"fr" => "Diplômes"
"en" => "Diplomas"
]
"year" => "2012"
"label" => array:2 [
"en" => "BSc in Statistics"
"fr" => "BSc en Statistiques"
]
"institution" => array:2 [
"fr" => "University College London"
"en" => "University College London"
]
"country" => array:2 [
"fr" => "Royaume-Uni"
"en" => "United Kingdom"
]
]
+lang: "fr"
+"parent": Essec\Faculty\Model\Profile {#2233}
}
]
"bio" => array:2 [
"fr" => null
"en" => null
]
"department" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"industrrySectors" => array:2 [
"fr" => null
"en" => null
]
"researchFields" => array:2 [
"fr" => "Inférence bayésienne - Statistiques de calcul - Méthodes de Monte Carlo - Statistiques informatiques (telles que les méthodes de Monte Carlo séquentiel et de Monte Carlo à chaîne de Markov) - Méthodes de transport (en particulier l'utilisation des flux) - Contrôle optimal (et son calcul efficace)"
"en" => "Bayesian inference - Computational statistics - Monte Carlo methods - Computational statistics (such as sequential Monte Carlo and Markov chain Monte Carlo methods) - Transport methods (in particular the use of flows) - Optimal control (and its efficient calculation)"
]
"teachingFields" => array:2 [
"fr" => "Théorie des probabilités et statistiques - Management - Systèmes d'aide à la décision - Analyse des données statistiques"
"en" => "Probability Theory & Mathematical Statistics - Management - Decision Support Systems - Statistical Data Analysis"
]
"distinctions" => array:1 [
0 => Essec\Faculty\Model\Distinction {#2239
#_index: null
#_id: null
#_source: array:6 [
"date" => "2022-12-01"
"label" => array:2 [
"fr" => "2022 Blackwell-Rosenbluth Award"
"en" => "2022 Blackwell-Rosenbluth Award"
]
"type" => array:2 [
"fr" => "Prix"
"en" => "Awards"
]
"tri" => " 1 "
"institution" => array:2 [
"fr" => "International Society for Bayesian Analysis,"
"en" => "International Society for Bayesian Analysis,"
]
"country" => array:2 [
"fr" => "États-Unis"
"en" => "United States of America"
]
]
+lang: "fr"
+"parent": Essec\Faculty\Model\Profile {#2233}
}
]
"teaching" => []
"otherActivities" => array:1 [
0 => Essec\Faculty\Model\ExtraActivity {#2234
#_index: null
#_id: null
#_source: array:9 [
"startDate" => "2022-01-01"
"endDate" => "2023-01-01"
"year" => null
"uuid" => "103"
"type" => array:2 [
"fr" => "Activités de recherche"
"en" => "Research activities"
]
"subType" => array:2 [
"fr" => "Membre d'un comité de lecture"
"en" => "Editorial Board Membership"
]
"label" => array:2 [
"fr" => "Co-Rédacteur en Chef de Statistics and Computing"
"en" => "Associate Editor of Statistics and Computing"
]
"institution" => array:2 [
"fr" => null
"en" => null
]
"country" => array:2 [
"fr" => null
"en" => null
]
]
+lang: "fr"
+"parent": Essec\Faculty\Model\Profile {#2233}
}
]
"theses" => []
"sitePerso" => "https://sites.google.com/view/jeremyheng/"
"indexedAt" => "2025-05-22T09:21:23.000Z"
"contributions" => array:21 [
0 => Essec\Faculty\Model\Contribution {#2241
#_index: "academ_contributions"
#_id: "6059"
#_source: array:18 [
"id" => "6059"
"slug" => "6059-gibbs-flow-for-approximate-transport-with-applications-to-bayesian-computation"
"yearMonth" => "2019-07"
"year" => "2019"
"title" => "Gibbs Flow for Approximate Transport with Applications to Bayesian Computation"
"description" => "HENG, J., POKERN, Y. et DOUCET, A. (2019). Gibbs Flow for Approximate Transport with Applications to Bayesian Computation. Dans: International Conference on Scientific Computation and Differential Equations (SciCADE 2019)."
"authors" => array:3 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
1 => array:1 [
"name" => "POKERN Y."
]
2 => array:1 [
"name" => "DOUCET Arnaud"
]
]
"ouvrage" => "International Conference on Scientific Computation and Differential Equations (SciCADE 2019)"
"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" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
1 => Essec\Faculty\Model\Contribution {#2243
#_index: "academ_contributions"
#_id: "2797"
#_source: array:18 [
"id" => "2797"
"slug" => "2797-unbiased-hamiltonian-monte-carlo-with-couplings"
"yearMonth" => "2019-02"
"year" => "2019"
"title" => "Unbiased Hamiltonian Monte Carlo with couplings"
"description" => "HENG, J. et JACOB, P. (2019). Unbiased Hamiltonian Monte Carlo with couplings. <i>Biometrika</i>, 106(2), pp. 287-302."
"authors" => array:2 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
1 => array:3 [
"name" => "JACOB Pierre"
"bid" => "B00795650"
"slug" => "jacob-pierre"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2023-07-10 17:25:47"
"publicationUrl" => "https://doi.org/10.1093/biomet/asy074"
"publicationInfo" => array:3 [
"pages" => "287-302"
"volume" => "106"
"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 propose a method for parallelization of Hamiltonian Monte Carlo estimators. Our approach involves constructing a pair of Hamiltonian Monte Carlo chains that are coupled in such a way that they meet exactly after some random number of iterations. These chains can then be combined so that the resulting estimators are unbiased. This allows us to produce independent replicates in parallel and average them to obtain estimators that are consistent in the limit of the number of replicates, rather than in the usual limit of the number of Markov chain iterations. We investigate the scalability of our coupling in high dimensions on a toy example. The choice of algorithmic parameters and the efficiency of our proposed approach are then illustrated on a logistic regression with 300 covariates and a log-Gaussian Cox point processes model with low- to fine-grained discretizations."
"en" => "We propose a method for parallelization of Hamiltonian Monte Carlo estimators. Our approach involves constructing a pair of Hamiltonian Monte Carlo chains that are coupled in such a way that they meet exactly after some random number of iterations. These chains can then be combined so that the resulting estimators are unbiased. This allows us to produce independent replicates in parallel and average them to obtain estimators that are consistent in the limit of the number of replicates, rather than in the usual limit of the number of Markov chain iterations. We investigate the scalability of our coupling in high dimensions on a toy example. The choice of algorithmic parameters and the efficiency of our proposed approach are then illustrated on a logistic regression with 300 covariates and a log-Gaussian Cox point processes model with low- to fine-grained discretizations."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
2 => Essec\Faculty\Model\Contribution {#2245
#_index: "academ_contributions"
#_id: "4453"
#_source: array:18 [
"id" => "4453"
"slug" => "4453-clustering-time-series-with-nonlinear-dynamics-a-bayesian-non-parametric-and-particle-based-approach"
"yearMonth" => "2019-04"
"year" => "2019"
"title" => "Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach"
"description" => "LIN, A., ZHANG, Y., HENG, J., ALLSOP, S.A., TYE, K.M. et JACOB, P. (2019). Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach. Dans: <i>Proceedings of Machine Learning Research</i>. "
"authors" => array:6 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
1 => array:3 [
"name" => "JACOB Pierre"
"bid" => "B00795650"
"slug" => "jacob-pierre"
]
2 => array:1 [
"name" => "LIN A"
]
3 => array:1 [
"name" => "ZHANG Y."
]
4 => array:1 [
"name" => "ALLSOP S. A."
]
5 => array:1 [
"name" => "TYE K. M."
]
]
"ouvrage" => "Proceedings of Machine Learning Research"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => "89"
"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" => "We propose a general statistical framework for clustering multiple time series that exhibit nonlinear dynamics into an a-priori-unknown number of sub-groups. Our motivation comes from neuroscience, where an important problem is to identify, within a large assembly of neurons, subsets that respond similarly to a stimulus or contingency. Upon modeling the multiple time series as the output of a Dirichlet process mixture of nonlinear state-space models, we derive a Metropolis-within-Gibbs algorithm for full Bayesian inference that alternates between sampling cluster assignments and sampling parameter values that form the basis of the clustering. The Metropolis step employs recent innovations in particle-based methods. We apply the framework to clustering time series acquired from the prefrontal cortex of mice in an experiment designed to characterize the neural underpinnings of fear."
"en" => "We propose a general statistical framework for clustering multiple time series that exhibit nonlinear dynamics into an a-priori-unknown number of sub-groups. Our motivation comes from neuroscience, where an important problem is to identify, within a large assembly of neurons, subsets that respond similarly to a stimulus or contingency. Upon modeling the multiple time series as the output of a Dirichlet process mixture of nonlinear state-space models, we derive a Metropolis-within-Gibbs algorithm for full Bayesian inference that alternates between sampling cluster assignments and sampling parameter values that form the basis of the clustering. The Metropolis step employs recent innovations in particle-based methods. We apply the framework to clustering time series acquired from the prefrontal cortex of mice in an experiment designed to characterize the neural underpinnings of fear."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
3 => Essec\Faculty\Model\Contribution {#2242
#_index: "academ_contributions"
#_id: "14213"
#_source: array:18 [
"id" => "14213"
"slug" => "14213-diffusion-schrodinger-bridge-with-applications-to-score-based-generative-modeling"
"yearMonth" => "2022-06"
"year" => "2022"
"title" => "Diffusion Schrodinger Bridge with Applications to Score-based Generative Modeling"
"description" => "HENG, J. (2022). Diffusion Schrodinger Bridge with Applications to Score-based Generative Modeling. Dans: 5th International Conference on Econometrics and Statistics (EcoSta) 2022. Kyoto."
"authors" => array:1 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
]
"ouvrage" => "5th International Conference on Econometrics and Statistics (EcoSta) 2022"
"keywords" => []
"updatedAt" => "2023-07-21 01:00:39"
"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" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
4 => Essec\Faculty\Model\Contribution {#2246
#_index: "academ_contributions"
#_id: "10698"
#_source: array:18 [
"id" => "10698"
"slug" => "10698-controlled-sequential-monte-carlo"
"yearMonth" => "2019-05"
"year" => "2019"
"title" => "Controlled Sequential Monte Carlo"
"description" => "HENG, J., BISHOP, A.N., DELIGIANNIDIS, G. et DOUCET, A. (2019). Controlled Sequential Monte Carlo. <i>Annals of Statistics</i>, 48(5), pp. 2904-2929."
"authors" => array:4 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
1 => array:1 [
"name" => "BISHOP Adrian N."
]
2 => array:1 [
"name" => "DELIGIANNIDIS George"
]
3 => array:1 [
"name" => "DOUCET Arnaud"
]
]
"ouvrage" => ""
"keywords" => array:6 [
0 => "State space models"
1 => "annealed importance sampling"
2 => "normalizing constants"
3 => "optimal control"
4 => "approximate dynamic programming"
5 => "reinforcement learning"
]
"updatedAt" => "2023-07-10 17:25:23"
"publicationUrl" => "https://projecteuclid.org/euclid.aos/1600480936"
"publicationInfo" => array:3 [
"pages" => "2904-2929"
"volume" => "48"
"number" => "5"
]
"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" => "Sequential Monte Carlo methods, also known as particle methods, are a popular set of techniques for approximating high-dimensional probability distributions and their normalizing constants. These methods have found numerous applications in statistics and related fields; for example, for inference in nonlinear non-Gaussian state space models, and in complex static models. Like many Monte Carlo sampling schemes, they rely on proposal distributions which crucially impact their performance. We introduce here a class of controlled sequential Monte Carlo algorithms, where the proposal distributions are determined by approximating the solution to an associated optimal control problem using an iterative scheme. This method builds upon a number of existing algorithms in econometrics, physics and statistics for inference in state space models, and generalizes these methods so as to accommodate complex static models. We provide a theoretical analysis concerning the fluctuation and stability of this methodology that also provides insight into the properties of related algorithms. We demonstrate significant gains over state-of-the-art methods at a fixed computational complexity on a variety of applications."
"en" => "Sequential Monte Carlo methods, also known as particle methods, are a popular set of techniques for approximating high-dimensional probability distributions and their normalizing constants. These methods have found numerous applications in statistics and related fields; for example, for inference in nonlinear non-Gaussian state space models, and in complex static models. Like many Monte Carlo sampling schemes, they rely on proposal distributions which crucially impact their performance. We introduce here a class of controlled sequential Monte Carlo algorithms, where the proposal distributions are determined by approximating the solution to an associated optimal control problem using an iterative scheme. This method builds upon a number of existing algorithms in econometrics, physics and statistics for inference in state space models, and generalizes these methods so as to accommodate complex static models. We provide a theoretical analysis concerning the fluctuation and stability of this methodology that also provides insight into the properties of related algorithms. We demonstrate significant gains over state-of-the-art methods at a fixed computational complexity on a variety of applications."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
5 => Essec\Faculty\Model\Contribution {#2240
#_index: "academ_contributions"
#_id: "10950"
#_source: array:18 [
"id" => "10950"
"slug" => "10950-multilevel-particle-filters-for-the-non-linear-filtering-problem-in-continuous-time"
"yearMonth" => "2020-06"
"year" => "2020"
"title" => "Multilevel Particle Filters for the Non-Linear Filtering Problem in Continuous Time"
"description" => "HENG, J., YU, F. et HENG, J. (2020). Multilevel Particle Filters for the Non-Linear Filtering Problem in Continuous Time. <i>Statistics and Computing</i>, 30, pp. 1381-1402."
"authors" => array:2 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
1 => array:1 [
"name" => "YU F."
]
]
"ouvrage" => ""
"keywords" => array:3 [
0 => "Multilevel Monte Carlo"
1 => "Particle filters"
2 => "Non-linear filtering"
]
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://www.researchgate.net/publication/342253685_Multilevel_particle_filters_for_the_non-linear_filtering_problem_in_continuous_time"
"publicationInfo" => array:3 [
"pages" => "1381-1402"
"volume" => "30"
"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 following article we consider the numerical approximation of the non-linear filter in continuous-time, where the observations and signal follow diffusion processes. Given access to high-frequency, but discrete-time observations, we resort to a first order time discretization of the non-linear filter, followed by an Euler discretization of the signal dynamics. In order to approximate the associated discretized non-linear filter, one can use a particle filter. Under assumptions, this can achieve a mean square error of \(\mathcal {O}(\epsilon ^2)\), for \(\epsilon >0\) arbitrary, such that the associated cost is \(\mathcal {O}(\epsilon ^{-4})\). We prove, under assumptions, that the multilevel particle filter of Jasra et al. (SIAM J Numer Anal 55:3068–3096, 2017) can achieve a mean square error of \(\mathcal {O}(\epsilon ^2)\), for cost \(\mathcal {O}(\epsilon ^{-3})\). This is supported by numerical simulations in several examples."
"en" => "In the following article we consider the numerical approximation of the non-linear filter in continuous-time, where the observations and signal follow diffusion processes. Given access to high-frequency, but discrete-time observations, we resort to a first order time discretization of the non-linear filter, followed by an Euler discretization of the signal dynamics. In order to approximate the associated discretized non-linear filter, one can use a particle filter. Under assumptions, this can achieve a mean square error of \(\mathcal {O}(\epsilon ^2)\), for \(\epsilon >0\) arbitrary, such that the associated cost is \(\mathcal {O}(\epsilon ^{-4})\). We prove, under assumptions, that the multilevel particle filter of Jasra et al. (SIAM J Numer Anal 55:3068–3096, 2017) can achieve a mean square error of \(\mathcal {O}(\epsilon ^2)\), for cost \(\mathcal {O}(\epsilon ^{-3})\). This is supported by numerical simulations in several examples."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
6 => Essec\Faculty\Model\Contribution {#2244
#_index: "academ_contributions"
#_id: "11158"
#_source: array:18 [
"id" => "11158"
"slug" => "11158-bayesian-estimation-of-long-run-risk-models-using-sequential-monte-carlo"
"yearMonth" => "2022-05"
"year" => "2022"
"title" => "Bayesian Estimation of Long-Run Risk Models Using Sequential Monte Carlo"
"description" => "FULOP, A., HENG, J., LI, J. et LIU, H. (2022). Bayesian Estimation of Long-Run Risk Models Using Sequential Monte Carlo. <i>Journal of Econometrics</i>, 228(1), pp. 62-84."
"authors" => array:4 [
0 => array:3 [
"name" => "FULOP Andras"
"bid" => "B00072302"
"slug" => "fulop-andras"
]
1 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
2 => array:1 [
"name" => "LI Junye"
]
3 => array:1 [
"name" => "LIU Hening"
]
]
"ouvrage" => ""
"keywords" => array:7 [
0 => "Asset Pricing"
1 => "Long-Run Risk"
2 => "Autoregressive Gamma Process"
3 => "Log-linearization"
4 => "Projection Methods"
5 => "Particle Filters"
6 => "Sequential Monte Carlo Sampler"
]
"updatedAt" => "2023-07-10 17:16:50"
"publicationUrl" => "https://www.sciencedirect.com/science/article/pii/S0304407621000531"
"publicationInfo" => array:3 [
"pages" => "62-84"
"volume" => "228"
"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" => "We propose a likelihood-based Bayesian method that exploits up-to-date sequential Monte Carlo methods to efficiently estimate long-run risk models in which the conditional variance of consumption growth follows either an autoregressive (AR) process or an autoregressive gamma (ARG) process. We use the U.S. quarterly consumption and asset returns data from the postwar period to implement estimation. Our findings are: (1) informative priors on the preference parameters can help to improve model performance; (2) expected consumption growth has a very persistent component, whereas consumption volatility is less persistent; (3) while the ARG-based model performs better than the AR-based one statistically, the latter could fit asset returns better; and (4) the solution method matters more for estimation in the AR-based model than in the ARG-based model."
"en" => "We propose a likelihood-based Bayesian method that exploits up-to-date sequential Monte Carlo methods to efficiently estimate long-run risk models in which the conditional variance of consumption growth follows either an autoregressive (AR) process or an autoregressive gamma (ARG) process. We use the U.S. quarterly consumption and asset returns data from the postwar period to implement estimation. Our findings are: (1) informative priors on the preference parameters can help to improve model performance; (2) expected consumption growth has a very persistent component, whereas consumption volatility is less persistent; (3) while the ARG-based model performs better than the AR-based one statistically, the latter could fit asset returns better; and (4) the solution method matters more for estimation in the AR-based model than in the ARG-based model."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
7 => Essec\Faculty\Model\Contribution {#2247
#_index: "academ_contributions"
#_id: "11159"
#_source: array:18 [
"id" => "11159"
"slug" => "11159-a-multilevel-approach-for-stochastic-nonlinear-optimal-control"
"yearMonth" => "2022-05"
"year" => "2022"
"title" => "A Multilevel Approach for Stochastic Nonlinear Optimal Control"
"description" => "JASRA, A., HENG, J., XU, Y. et BISHOP, A.N. (2022). A Multilevel Approach for Stochastic Nonlinear Optimal Control. <i>International Journal of Control</i>, 95(5), pp. 1290-1304."
"authors" => array:3 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
1 => array:1 [
"name" => "XU Yaxian"
]
2 => array:1 [
"name" => "BISHOP Adrian N."
]
]
"ouvrage" => ""
"keywords" => array:4 [
0 => "optimal control"
1 => "multilevel Monte Carlo"
2 => "Markov chain Monte Carlo"
3 => "sequential Monte Carlo"
]
"updatedAt" => "2023-07-10 17:17:38"
"publicationUrl" => "https://www.tandfonline.com/doi/abs/10.1080/00207179.2020.1849805?journalCode=tcon20"
"publicationInfo" => array:3 [
"pages" => "1290-1304"
"volume" => "95"
"number" => "5"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => "Royaume-Uni"
"en" => "United Kingdom"
]
"abstract" => array:2 [
"fr" => "We consider a class of finite-time horizon nonlinear stochastic optimal control problem. Although the optimal control admits a path integral representation for this class of control problems, efficient computation of the associated path integrals remains a challenging task. We propose a new Monte Carlo approach that significantly improves upon existing methodology. We tackle the issue of exponential growth in variance with the time horizon by casting optimal control estimation as a smoothing problem for a state-space model, and applying smoothing algorithms based on particle Markov chain Monte Carlo. To further reduce the cost, we then develop a multilevel Monte Carlo method which allows us to obtain an estimator of the optimal control with O(ϵ2) mean squared error with a cost of O(ϵ−2log(ϵ)2). In contrast, a cost of O(ϵ−3) is required for the existing methodology to achieve the same mean squared error. Our approach is illustrated on two numerical examples."
"en" => "We consider a class of finite-time horizon nonlinear stochastic optimal control problem. Although the optimal control admits a path integral representation for this class of control problems, efficient computation of the associated path integrals remains a challenging task. We propose a new Monte Carlo approach that significantly improves upon existing methodology. We tackle the issue of exponential growth in variance with the time horizon by casting optimal control estimation as a smoothing problem for a state-space model, and applying smoothing algorithms based on particle Markov chain Monte Carlo. To further reduce the cost, we then develop a multilevel Monte Carlo method which allows us to obtain an estimator of the optimal control with O(ϵ2) mean squared error with a cost of O(ϵ−2log(ϵ)2). In contrast, a cost of O(ϵ−3) is required for the existing methodology to achieve the same mean squared error. Our approach is illustrated on two numerical examples."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
8 => Essec\Faculty\Model\Contribution {#2248
#_index: "academ_contributions"
#_id: "12111"
#_source: array:18 [
"id" => "12111"
"slug" => "12111-gibbs-flow-for-approximate-transport-with-applications-to-bayesian-computation"
"yearMonth" => "2021-02"
"year" => "2021"
"title" => "Gibbs flow for approximate transport with applications to Bayesian computation"
"description" => "HENG, J., DOUCET, A. et POKERN, Y. (2021). Gibbs flow for approximate transport with applications to Bayesian computation. <i>Journal of the Royal Statistical Society: Series B (Statistical Methodology)</i>, 83(1), pp. 157-187."
"authors" => array:3 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
1 => array:1 [
"name" => "DOUCET Arnaud"
]
2 => array:1 [
"name" => "POKERN Yvo"
]
]
"ouvrage" => ""
"keywords" => array:5 [
0 => "Markoc chain"
1 => "Monte Carlo"
2 => "mass transport"
3 => "normalising constants"
4 => "path sampling"
]
"updatedAt" => "2024-10-31 13:51:19"
"publicationUrl" => "https://doi.org/10.1111/rssb.12404"
"publicationInfo" => array:3 [
"pages" => "157-187"
"volume" => "83"
"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" => "We show here how to build a tractable approximation of a novel transport map. Even when this ordinary differential equation is time‐discretised and the full conditional distributions are numerically approximated, the resulting distribution of mapped samples can be efficiently evaluated and used as a proposal within sequential Monte Carlo samplers. We demonstrate significant gains over state‐of‐the‐art sequential Monte Carlo samplers at a fixed computational complexity on a variety of applications."
"en" => "We show here how to build a tractable approximation of a novel transport map. Even when this ordinary differential equation is time‐discretised and the full conditional distributions are numerically approximated, the resulting distribution of mapped samples can be efficiently evaluated and used as a proposal within sequential Monte Carlo samplers. We demonstrate significant gains over state‐of‐the‐art sequential Monte Carlo samplers at a fixed computational complexity on a variety of applications."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
9 => Essec\Faculty\Model\Contribution {#2249
#_index: "academ_contributions"
#_id: "12537"
#_source: array:18 [
"id" => "12537"
"slug" => "12537-clustering-time-series-with-nonlinear-dynamics-a-bayesian-non-parametric-and-particle-based-approach"
"yearMonth" => "2019-01"
"year" => "2019"
"title" => "Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach"
"description" => "JACOB, P., LIN, A., ZHANG, Y., HENG, J., ALLSOP, S.A., TYE, K.M. et BA, D. (2019). Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach. Dans: <i>The 22nd International Conference on Artificial Intelligence and Statistics</i>. Proceedings of Machine Learning Research, pp. 2476-2484."
"authors" => array:7 [
0 => array:3 [
"name" => "JACOB Pierre"
"bid" => "B00795650"
"slug" => "jacob-pierre"
]
1 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
2 => array:1 [
"name" => "LIN Alexander"
]
3 => array:1 [
"name" => "ZHANG Yingzhuo"
]
4 => array:1 [
"name" => "ALLSOP Stephen A."
]
5 => array:1 [
"name" => "TYE K. M."
]
6 => array:1 [
"name" => "BA Demba"
]
]
"ouvrage" => "The 22nd International Conference on Artificial Intelligence and Statistics"
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => "2476-2484"
"volume" => "89"
"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" => "Royaume-Uni"
"en" => "United Kingdom"
]
"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" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
10 => Essec\Faculty\Model\Contribution {#2250
#_index: "academ_contributions"
#_id: "12798"
#_source: array:18 [
"id" => "12798"
"slug" => "12798-efficient-likelihood-based-estimation-via-annealing-for-dynamic-structural-macrofinance-models"
"yearMonth" => "2021-12"
"year" => "2021"
"title" => "Efficient Likelihood-based Estimation via Annealing for Dynamic Structural Macrofinance Models"
"description" => "FULOP, A., HENG, J. et LI, Y. (2021). Efficient Likelihood-based Estimation via Annealing for Dynamic Structural Macrofinance Models. Dans: 2021 European Winter Meetings of the Econometric Society. Barcelona."
"authors" => array:3 [
0 => array:3 [
"name" => "FULOP Andras"
"bid" => "B00072302"
"slug" => "fulop-andras"
]
1 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
2 => array:3 [
"name" => "LI Yan"
"bid" => "B00132135"
"slug" => "li-yan"
]
]
"ouvrage" => "2021 European Winter Meetings of the Econometric Society"
"keywords" => []
"updatedAt" => "2023-01-27 01:00:42"
"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" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
11 => Essec\Faculty\Model\Contribution {#2251
#_index: "academ_contributions"
#_id: "13049"
#_source: array:18 [
"id" => "13049"
"slug" => "13049-an-invitation-to-sequential-monte-carlo-samplers"
"yearMonth" => "2022-07"
"year" => "2022"
"title" => "An invitation to sequential Monte Carlo samplers"
"description" => "DAI, C., HENG, J., JACOB, P. et WHITELEY, N. (2022). An invitation to sequential Monte Carlo samplers. <i>Journal of the American Statistical Association</i>, 117(539), pp. 1587-1600."
"authors" => array:4 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
1 => array:3 [
"name" => "JACOB Pierre"
"bid" => "B00795650"
"slug" => "jacob-pierre"
]
2 => array:1 [
"name" => "DAI Chenguang"
]
3 => array:1 [
"name" => "WHITELEY Nick"
]
]
"ouvrage" => ""
"keywords" => array:4 [
0 => "Monte Carlo methods"
1 => "sequential inference"
2 => "normalizing constant"
3 => "interacting particle systems"
]
"updatedAt" => "2023-07-18 16:16:04"
"publicationUrl" => "https://www.tandfonline.com/doi/full/10.1080/01621459.2022.2087659"
"publicationInfo" => array:3 [
"pages" => "1587-1600"
"volume" => "117"
"number" => "539"
]
"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" => "Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits."
"en" => "Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
12 => Essec\Faculty\Model\Contribution {#2252
#_index: "academ_contributions"
#_id: "13219"
#_source: array:18 [
"id" => "13219"
"slug" => "13219-unbiased-hamiltonian-monte-carlo-with-couplings"
"yearMonth" => "2019-06"
"year" => "2019"
"title" => "Unbiased Hamiltonian Monte Carlo with couplings"
"description" => "HENG, J. et JACOB, P. (2019). Unbiased Hamiltonian Monte Carlo with couplings. Biometrika."
"authors" => array:2 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
1 => array:3 [
"name" => "JACOB Pierre"
"bid" => "B00795650"
"slug" => "jacob-pierre"
]
]
"ouvrage" => ""
"keywords" => array:4 [
0 => "Coupling"
1 => "Hamiltonian Monte Carlo method"
2 => "Parallel computing"
3 => "Unbiased estimation"
]
"updatedAt" => "2024-10-31 13:51:19"
"publicationUrl" => "https://doi.org/10.1093/biomet/asy074"
"publicationInfo" => array:3 [
"pages" => "769"
"volume" => "106"
"number" => "2"
]
"type" => array:2 [
"fr" => "Autre"
"en" => "Other"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "This is a correction to: Biometrika, Volume 106, Issue 2, June 2019, Pages 287–302, https://doi.org/10.1093/biomet/asy074."
"en" => "This is a correction to: Biometrika, Volume 106, Issue 2, June 2019, Pages 287–302, https://doi.org/10.1093/biomet/asy074."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
13 => Essec\Faculty\Model\Contribution {#2253
#_index: "academ_contributions"
#_id: "13386"
#_source: array:18 [
"id" => "13386"
"slug" => "13386-on-unbiased-estimation-for-discretized-models"
"yearMonth" => "2023-06"
"year" => "2023"
"title" => "On Unbiased Estimation for Discretized Models"
"description" => "HENG, J., JASRA, A., LAW, K. et TARAKANOV, A. (2023). On Unbiased Estimation for Discretized Models. <i>SIAM/ASA Journal on Uncertainty Quantification</i>, 11(2), pp. 10.1137/21M1460788."
"authors" => array:4 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
1 => array:1 [
"name" => "JASRA Ajay"
]
2 => array:1 [
"name" => "LAW Kody"
]
3 => array:1 [
"name" => "TARAKANOV Alexander"
]
]
"ouvrage" => ""
"keywords" => array:4 [
0 => "Randomization methods"
1 => "Markov chain"
2 => "Monte Carlo"
3 => "Bayesian inverse problems"
]
"updatedAt" => "2024-03-18 09:52:43"
"publicationUrl" => "https://doi.org/10.1137/21M1460788"
"publicationInfo" => array:3 [
"pages" => "10.1137/21M1460788"
"volume" => "11"
"number" => "2"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => "États-Unis"
"en" => "United States of America"
]
"abstract" => array:2 [
"fr" => "In this article, we consider computing expectations w.r.t. probability measures which are subject to discretization error. Examples include partially observed diffusion processes or inverse problems, where one may have to discretize time and/or space in order to practically work with the probability of interest. Given access only to these discretizations, we consider the construction of unbiased Monte Carlo estimators of expectations w.r.t. such target probability distributions. It is shown how to obtain such estimators using a novel adaptation of randomization schemes and Markov simulation methods. Under appropriate assumptions, these estimators possess finite variance and finite expected cost. There are two important consequences of this approach: (i) unbiased inference is achieved at the canonical complexity rate, and (ii) the resulting estimators can be generated independently, thereby allowing strong scaling to arbitrarily many parallel processors. Several algorithms are presented and applied to some examples of Bayesian inference problems with both simulated and real observed data."
"en" => "In this article, we consider computing expectations w.r.t. probability measures which are subject to discretization error. Examples include partially observed diffusion processes or inverse problems, where one may have to discretize time and/or space in order to practically work with the probability of interest. Given access only to these discretizations, we consider the construction of unbiased Monte Carlo estimators of expectations w.r.t. such target probability distributions. It is shown how to obtain such estimators using a novel adaptation of randomization schemes and Markov simulation methods. Under appropriate assumptions, these estimators possess finite variance and finite expected cost. There are two important consequences of this approach: (i) unbiased inference is achieved at the canonical complexity rate, and (ii) the resulting estimators can be generated independently, thereby allowing strong scaling to arbitrarily many parallel processors. Several algorithms are presented and applied to some examples of Bayesian inference problems with both simulated and real observed data."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
14 => Essec\Faculty\Model\Contribution {#2254
#_index: "academ_contributions"
#_id: "13387"
#_source: array:18 [
"id" => "13387"
"slug" => "13387-diffusion-schrodinger-bridge-with-applications-to-score-based-generative-modeling"
"yearMonth" => "2021-12"
"year" => "2021"
"title" => "Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling"
"description" => "DE BORTOLI, V., THORNTON, J., HENG, J. et DOUCET, A. (2021). Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling. Dans: <i>NeurIPS 2021</i>. Proceedings of Machine Learning Research."
"authors" => array:4 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
1 => array:1 [
"name" => "DE BORTOLI Valentin"
]
2 => array:1 [
"name" => "THORNTON James"
]
3 => array:1 [
"name" => "DOUCET A."
]
]
"ouvrage" => "NeurIPS 2021"
"keywords" => []
"updatedAt" => "2022-12-06 10:15:31"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => ""
"volume" => ""
"number" => ""
]
"type" => array:2 [
"fr" => "Actes d'une conférence"
"en" => "Conference Proceedings"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => "Royaume-Uni"
"en" => "United Kingdom"
]
"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" => "2025-05-22T09:21:44.000Z"
]
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}
15 => Essec\Faculty\Model\Contribution {#2255
#_index: "academ_contributions"
#_id: "14100"
#_source: array:18 [
"id" => "14100"
"slug" => "14100-computational-doob-h-transforms-for-online-filtering-of-discretely-observed-diffusions"
"yearMonth" => "2023-07"
"year" => "2023"
"title" => "Computational Doob h-transforms for Online Filtering of Discretely Observed Diffusions"
"description" => "CHOPIN, N., FULOP, A., HENG, J. et THIERY, A.H. (2023). Computational Doob h-transforms for Online Filtering of Discretely Observed Diffusions. Dans: <i>Proceedings of the 40th International Conference on Machine Learning, PMLR 202:5904-5923</i>. Honolulu: Proceedings of Machine Learning Research."
"authors" => array:4 [
0 => array:3 [
"name" => "FULOP Andras"
"bid" => "B00072302"
"slug" => "fulop-andras"
]
1 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
2 => array:1 [
"name" => "CHOPIN Nicolas"
]
3 => array:1 [
"name" => "THIERY Alexandre H."
]
]
"ouvrage" => "Proceedings of the 40th International Conference on Machine Learning, PMLR 202:5904-5923"
"keywords" => array:10 [
0 => "Computational Doob h-transforms"
1 => "Online filtering"
2 => "Discretely observed diffusions"
3 => "Machine learning"
4 => "Stochastic processes"
5 => "Bayesian filtering"
6 => "State estimation"
7 => "Hidden Markov models"
8 => "Sequential Monte Carlo methods"
9 => "Probabilistic inference"
]
"updatedAt" => "2024-10-31 13:51:19"
"publicationUrl" => "https://proceedings.mlr.press/v202/"
"publicationInfo" => array:3 [
"pages" => null
"volume" => "202"
"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" => "Royaume-Uni"
"en" => "United Kingdom"
]
"abstract" => array:2 [
"fr" => "This paper is concerned with online filtering of discretely observed nonlinear diffusion processes. Our approach is based on the fully adapted auxiliary particle filter, which involves Doob’s htransforms that are typically intractable. We propose a computational framework to approximate these h-transforms by solving the underlying backward Kolmogorov equations using nonlinear Feynman-Kac formulas and neural networks. The methodology allows one to train a locally optimal particle filter prior to the data-assimilation procedure. Numerical experiments illustrate that the proposed approach can be orders of magnitude more efficient than state-of-the-art particle f ilters in the regime of highly informative observations, when the observations are extreme under the model, or if the state dimension is large."
"en" => "This paper is concerned with online filtering of discretely observed nonlinear diffusion processes. Our approach is based on the fully adapted auxiliary particle filter, which involves Doob’s htransforms that are typically intractable. We propose a computational framework to approximate these h-transforms by solving the underlying backward Kolmogorov equations using nonlinear Feynman-Kac formulas and neural networks. The methodology allows one to train a locally optimal particle filter prior to the data-assimilation procedure. Numerical experiments illustrate that the proposed approach can be orders of magnitude more efficient than state-of-the-art particle f ilters in the regime of highly informative observations, when the observations are extreme under the model, or if the state dimension is large."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
16 => Essec\Faculty\Model\Contribution {#2256
#_index: "academ_contributions"
#_id: "14214"
#_source: array:18 [
"id" => "14214"
"slug" => "14214-artificial-intelligence-data-challenges"
"yearMonth" => "2022-05"
"year" => "2022"
"title" => "Artificial Intelligence, Data challenges"
"description" => "NIANQUIOA, J., HENG, J. et JACOB, P. (2022). Artificial Intelligence, Data challenges. Dans: 2022 Institute of Mathematical Statistics (IMS) Annual Meeting in Probability and Statistics. London."
"authors" => array:3 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
1 => array:3 [
"name" => "JACOB Pierre"
"bid" => "B00795650"
"slug" => "jacob-pierre"
]
2 => array:1 [
"name" => "NIANQUIOA J."
]
]
"ouvrage" => "2022 Institute of Mathematical Statistics (IMS) Annual Meeting in Probability and Statistics"
"keywords" => []
"updatedAt" => "2023-07-21 01:00:39"
"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" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
17 => Essec\Faculty\Model\Contribution {#2257
#_index: "academ_contributions"
#_id: "14809"
#_source: array:18 [
"id" => "14809"
"slug" => "14809-diffusion-schrodinger-bridges-for-bayesian-computation"
"yearMonth" => "2024-02"
"year" => "2024"
"title" => "Diffusion Schrödinger Bridges for Bayesian Computation"
"description" => "HENG, J., DE BORTOLI, V. et DOUCET, A. (2024). Diffusion Schrödinger Bridges for Bayesian Computation. <i>Statistical Science</i>, 39(1), pp. 90-99."
"authors" => array:3 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
1 => array:1 [
"name" => "DE BORTOLI Valentin"
]
2 => array:1 [
"name" => "DOUCET Arnaud"
]
]
"ouvrage" => ""
"keywords" => array:5 [
0 => "Optimal transport"
1 => "Schrödinger bridge"
2 => "score matching"
3 => "Stochastic differential equation"
4 => "Time reversal"
]
"updatedAt" => "2025-03-26 10:25:18"
"publicationUrl" => "https://doi.org/10.1214/23-STS908"
"publicationInfo" => array:3 [
"pages" => "90-99"
"volume" => "39"
"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" => "Denoising diffusion models are a novel class of generative models that have recently become extremely popular in machine learning. In this paper, we describe how such ideas can also be used to sample from posterior distributions and, more generally, any target distribution whose density is known up to a normalizing constant. The key idea is to consider a forward “noising” diffusion initialized at the target distribution, which “transports” this latter to a normal distribution for long diffusion times. The time reversal of this process, the “denoising” diffusion, thus “transports” the normal distribution to the target distribution and can be approximated so as to sample from the target. To accelerate simulation, we show how one can introduce and approximate a Schrödinger bridge between these two distributions, that is, a diffusion which transports the normal to the target in finite time."
"en" => "Denoising diffusion models are a novel class of generative models that have recently become extremely popular in machine learning. In this paper, we describe how such ideas can also be used to sample from posterior distributions and, more generally, any target distribution whose density is known up to a normalizing constant. The key idea is to consider a forward “noising” diffusion initialized at the target distribution, which “transports” this latter to a normal distribution for long diffusion times. The time reversal of this process, the “denoising” diffusion, thus “transports” the normal distribution to the target distribution and can be approximated so as to sample from the target. To accelerate simulation, we show how one can introduce and approximate a Schrödinger bridge between these two distributions, that is, a diffusion which transports the normal to the target in finite time."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
18 => Essec\Faculty\Model\Contribution {#2258
#_index: "academ_contributions"
#_id: "14810"
#_source: array:18 [
"id" => "14810"
"slug" => "14810-on-unbiased-score-estimation-for-partially-observed-diffusions"
"yearMonth" => "2024-01"
"year" => "2024"
"title" => "On Unbiased Score Estimation for Partially Observed Diffusions"
"description" => "HENG, J., HOUSSINEAU, J. et JASRA, A. (2024). On Unbiased Score Estimation for Partially Observed Diffusions. <i>Journal of Machine Learning Research</i>, 25(66), pp. 1-66."
"authors" => array:3 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
1 => array:1 [
"name" => "HOUSSINEAU Jeremie"
]
2 => array:1 [
"name" => "JASRA Ajay"
]
]
"ouvrage" => ""
"keywords" => array:5 [
0 => "diffusions"
1 => "unbiased estimation"
2 => "particle filters"
3 => "coupling"
4 => "stochastic gradient methods"
]
"updatedAt" => "2024-10-31 13:51:19"
"publicationUrl" => "https://www.jmlr.org/papers/v25/23-0347.html"
"publicationInfo" => array:3 [
"pages" => "1-66"
"volume" => "25"
"number" => "66"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => "États-Unis"
"en" => "United States of America"
]
"abstract" => array:2 [
"fr" => "We consider a class of diffusion processes with finite-dimensional parameters and partially observed at discrete time instances. We propose a methodology to unbiasedly estimate the expectation of a given functional of the diffusion process conditional on parameters and data. When these unbiased estimators with appropriately chosen functionals are employed within an expectation-maximization algorithm or a stochastic gradient method, this enables statistical inference using the maximum likelihood or Bayesian framework. Compared to existing approaches, the use of our unbiased estimators allows one to remove any time-discretization bias and Markov chain Monte Carlo burn-in bias. Central to our methodology is a novel and natural combination of multilevel randomization schemes and unbiased Markov chain Monte Carlo methods, and the development of new couplings of multiple conditional particle filters. We establish under assumptions that our estimators are unbiased and have finite variance. We illustrate various aspects of our method on an Ornstein--Uhlenbeck model, a logistic diffusion model for population dynamics, and a neural network model for grid cells."
"en" => "We consider a class of diffusion processes with finite-dimensional parameters and partially observed at discrete time instances. We propose a methodology to unbiasedly estimate the expectation of a given functional of the diffusion process conditional on parameters and data. When these unbiased estimators with appropriately chosen functionals are employed within an expectation-maximization algorithm or a stochastic gradient method, this enables statistical inference using the maximum likelihood or Bayesian framework. Compared to existing approaches, the use of our unbiased estimators allows one to remove any time-discretization bias and Markov chain Monte Carlo burn-in bias. Central to our methodology is a novel and natural combination of multilevel randomization schemes and unbiased Markov chain Monte Carlo methods, and the development of new couplings of multiple conditional particle filters. We establish under assumptions that our estimators are unbiased and have finite variance. We illustrate various aspects of our method on an Ornstein--Uhlenbeck model, a logistic diffusion model for population dynamics, and a neural network model for grid cells."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
19 => Essec\Faculty\Model\Contribution {#2259
#_index: "academ_contributions"
#_id: "14916"
#_source: array:18 [
"id" => "14916"
"slug" => "14916-statistical-inference-for-individual-based-models-of-disease-transmission"
"yearMonth" => "2024-06"
"year" => "2024"
"title" => "Statistical Inference for Individual-based Models of Disease Transmission"
"description" => "JU, N., HENG, J. et JACOB, P. (2024). Statistical Inference for Individual-based Models of Disease Transmission. Dans: 2024 Eastern Asia Chapter International Society for Bayesian Analysis Conference. Hong Kong."
"authors" => array:3 [
0 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
1 => array:3 [
"name" => "JACOB Pierre"
"bid" => "B00795650"
"slug" => "jacob-pierre"
]
2 => array:1 [
"name" => "JU Nianqiao"
]
]
"ouvrage" => "2024 Eastern Asia Chapter International Society for Bayesian Analysis Conference"
"keywords" => []
"updatedAt" => "2024-07-16 19:21:55"
"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" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
+"parent": null
}
20 => Essec\Faculty\Model\Contribution {#2260
#_index: "academ_contributions"
#_id: "14942"
#_source: array:18 [
"id" => "14942"
"slug" => "14942-computational-doobs-h-transforms-for-online-filtering"
"yearMonth" => "2024-03"
"year" => "2024"
"title" => "Computational Doob's h-transforms for Online Filtering"
"description" => "CHOPIN, N., FULOP, A., HENG, J. et THIERY, A.H. (2024). Computational Doob's h-transforms for Online Filtering. Dans: 6th Workshop on Sequential Monte Carlo Methods 2024. Edinburgh."
"authors" => array:4 [
0 => array:3 [
"name" => "FULOP Andras"
"bid" => "B00072302"
"slug" => "fulop-andras"
]
1 => array:3 [
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
]
2 => array:1 [
"name" => "CHOPIN Nicolas"
]
3 => array:1 [
"name" => "THIERY Alexandre H."
]
]
"ouvrage" => "6th Workshop on Sequential Monte Carlo Methods 2024"
"keywords" => []
"updatedAt" => "2025-02-13 17:20:38"
"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" => "2025-05-22T09:21:44.000Z"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 6.129635
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}
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2 => "<a href="https://sites.google.com/view/jeremyheng/" target="_blank">Personal site</a>"
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"docTitle" => "Jeremy HENG"
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