KLOPP Olga

2017 Associated Professor at ESSEC Business School, Cergy

2017 Member of CNRS (6 month) at CREST, Paris

2012 - 2017 Assistant Professor University Paris Ouest Nanterre la Défense

2010 - 2012 Postdoctoral fellow in CREST, Paris

2004 - 2008 Assistant Professor UNAM (Mexico)

• 2016 : HDR (Université Paris-Nanterre, France)
• 2004 : Ph.D. in Mathematics (Université Nationale Autonome de Mexico, Mexico)
• 1997 : Master in Mathematics and Applied Mathematics (Université d'État Lomonossov de Moscou, Russia)

Full-time academic appointments

• 2017 - Present : Associate Professor (ESSEC Business School, France)
• 2012 - 2017 : Assistant professor (Université Paris-Nanterre, France)
• 2004 - 2008 : Assistant professor (Universidad Nacional Autónoma de México (UNAM), Mexico)

Other Academic Appointments

• 2017 : Member of CNRS (Center for Research in Economics and Statistics (CREST) , France)
• 2010 - 2012 : Postdoctoral fellow (Center for Research in Economics and Statistics (CREST) , France)
• 2018 - Present : Scientific Track Head (ESSEC Business School, France)
• 2018 - 2022 : Pedagogical Head DD CentraleSupelec et ENSAE (ESSEC Business School, France)

• GAUCHER, S., KLOPP, O. and ROBIN, G. (2021). Outlier detection in networks with missing links. Computational Statistics and Data Analysis, 164, pp. 107308.
• GAUCHER, S. and KLOPP, O. (2021). Maximum likelihood estimation of sparse networks with missing observations. Journal of Statistical Planning and Inference, 215, pp. 299-329.
• ROBIN, G., KLOPP, O., JOSSE, J., MOULINES, E. and TIBSHIRANI, R. (2019). Main Effects and Interactions in Mixed and Incomplete Data Frames. Journal of the American Statistical Association, 115(531), pp. 1292-1303.
• KLOPP, O., LU, Y., TSYBAKOV, A.B. and ZHOU, H.H. (2019). Structured Matrix Estimation and Completion. Bernoulli: A Journal of Mathematical Statistics and Probability, 4B(25), pp. 3883-3911.
• KLOPP, O. and VERZELEN, N. (2018). Optimal Graphon Estimation in Cut Distance. Probability Theory and Related Fields, 174, pp. 1033-1090.
• KLOPP, O., CARPENTIER, A., LÖFFLER, M. and NICKL, R. (2018). Adaptive confidence sets for matrix completion. Bernoulli: A Journal of Mathematical Statistics and Probability, 24(4A), pp. 2429-2460.
• KLOPP, O. and GAIFFAS, S. (2017). High dimensional matrix estimation with unknown variance of the noise. Statistica Sinica, 27(1), pp. 115-145.
• KLOPP, O., TSYBAKOV, A. and VERZELEN, N. (2017). Oracle inequalities for network models and sparse graphon estimation. Annals of Statistics, 45(1), pp. 316-354.
• KLOPP, O., LOUNICI, K. and TSYBAKOV, A. (2017). Robust Matrix Completion. Probability Theory and Related Fields, 169(43862), pp. 523-564.
• KLOPP, O., LAFOND, J., MOULINES, E. and SALMON, J. (2015). Adaptive Multinomial Matrix Completion. The Electronic Journal of Statistics, 9(2), pp. 2950-2975.
• KLOPP, O. and TSYBAKOV, A. (2015). Estimation of matrices with row sparsity. Problems of Information Transmission, 51(4), pp. 335-348.
• KLOPP, O. (2015). Matrix completion by singular value thresholding : sharp bounds. The Electronic Journal of Statistics, 9(2), pp. 2348-2369.
• KLOPP, O. and PENSKY, M. (2015). Sparse high-dimensional varying coefficient model : non-asymptotic minimax study. Annals of Statistics, 43(3), pp. 1273-1299.
• KLOPP, O. (2014). Noisy low-rank matrix completion with general sampling distribution. Bernoulli: A Journal of Mathematical Statistics and Probability, 20(1), pp. 282-303.
• KLOPP, O. and PENSKY, M. (2013). Non-asymptotic approach to varying coefficient model. The Electronic Journal of Statistics, 7, pp. 454-479.
• KLOPP, O. (2011). Rank penalized estimators for high-dimensional matrices. The Electronic Journal of Statistics, 5, pp. 1161-1183.

• CARPENTIER, A., KLOPP, O. and LÖFFLER, M. (2019). Constructing Confidence Sets for the Matrix Completion Problem. In: Nonparametric Statistics. 1st ed. Springer, pp. 103-119.

• KLOPP, O. (2016). HDR. France.

• ROBIN, G., WAI, H.T., JOSSE, J., KLOPP, O. and MOULINES, A. (2018). Low-Rank Interactions and Sparse Additive Effects Model for Large Data Frames. In: Advances in Neural Information Processing Systems 31 (NIPS 2018).
• KLOPP, O., LAFOND, J., MOULINES, E. and SALMON, J. (2014). Probabilistic low-rank matrix completion on finite alphabets. In: NIPS. Montréal: Neural Information Processing Systems.

• KLOPP, O. (2020). Link Prediction in Sparse Graphon Model. In: 2020 EURANDOM Workshop: Graph Limits.
• KLOPP, O. (2020). Robust network analysis. In: 13th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2020), Virtual Conference.
• KLOPP, O. (2019). Matrix Completion: Old and New. In: 2019 Structural Inference in High-Dimensional Models 2.
• KLOPP, O. (2019). Sparse Network Estimation. In: 2019 The Power of Graphs in Machine Learning and Sequential Decision -making.
• KLOPP, O. (2019). Sparse Network Estimation. In: High dimensional probability and algorithms.
• KLOPP, O. (2019). Modèles de réseaux parcimonieux. In: 2019 Colloquium de Montpellier.
• KLOPP, O., TSYBAKOV, A. and VERZELEN, N. (2018). Network Models and Sparse Graphon Estimation. In: 14th Franco-Romanian Conference on Applied Mathematics.
• KLOPP, O. and VERZELEN, N. (2018). Optimal Graphon Estimation in Cut Distance. In: 27th Nordic Conference in Mathematical Statistics (NORDSTAT) 2018.
• KLOPP, O. and VERZELEN, N. (2018). Optimal Graphon Estimation in Cut Distance. In: Tercera jornada Franco-Chilena de Estadística.
• KLOPP, O., LOUNICI, K., TSYBAKOV, A.B. and ALAYA, M.Z. (2018). Robust Matrix Completion/Collective Matrix Completion. In: 40th Conference on Stochastic Processes and their Applications (SPA 2018).
• KLOPP, O., LU, Y., TSYBAKOV, A.B. and ZHOU, H.H. (2018). Structured Matrix Estimation and Completion. In: 4th Conference of the International Society for Nonparametric Statistics.
• KLOPP, O., TSYBAKOV, A. and VERZELEN, N. (2018). Network models. In: Tercera jornada Franco-Chilena de Estadística. Valparaiso.
• KLOPP, O., TSYBAKOV, A. and VERZELEN, N. (2018). Network models and sparse graphon estimation. In: NordStat 2018. Tartu.
• KLOPP, O., LOUNICI, K., TSYBAKOV, A. and ALAYA, M. (2018). Variety and Veracity of the Data in Matrix Completion. In: The 40th Conference on Stochastic Processes and their Applications. Gothenburg.
• KLOPP, O. and VERZELEN, N. (2017). Optimal Graphon Estimation in Cut Distance. In: Workshop on Community Detection and Network Reconstruction 2017.
• KLOPP, O. (2017). Optimal graphon estimation in cut distance. In: The Foundations in Computational Mathematics Conference. Barcelona.
• KLOPP, O. (2017). Optimal graphon estimation in cut distance. In: Statistics meets Stochastics 2. Moscow.
• KLOPP, O. (2016). Confidence sets for matrix completion. In: Advances in nonparametric and high-dimensional Statistic. Frejus.
• KLOPP, O. (2016). Matrix Completion. In: Multimedia Inpainting Workshop. Rennes.
• KLOPP, O. (2016). Network models and sparse graphon estimation. In: NIPS workshop. Barcelona.
• KLOPP, O. (2016). Oracle inequalities for network models and sparse graphon estimation. In: Joint Statistical Meeting. Chicago.
• KLOPP, O. (2016). Oracle inequalities for network models and sparse graphon estimation. In: 3rd ISNPS conference. Avignon.
• KLOPP, O. (2016). Oracle inequalities for network models and sparse graphon estimation. In: Modern problems of stochastic analysis and statistics. Moscow.
• KLOPP, O. (2015). 1-bit Matrix Completion. In: Indian Russian Conference in Statistics and Probability. Delhi.
• KLOPP, O. (2015). Oracle inequalities for network models and sparse graphon estimation. In: Meeting in Mathematical Statistics. Frejus.
• KLOPP, O. (2015). Oracle inequalities for network models and sparse graphon estimation. In: 2nd Heidelberg - Mannheim Stochastic Colloquium.
• KLOPP, O. (2015). Robust Matrix Completion. In: ISNPS Biosciences, Medicine, and novel Non-parametric Methods. Graz.
• KLOPP, O. (2015). Robust Matrix Completion. In: The First International Conference on missing values MissData. Rennes.

• KLOPP, O. and GAUCHER, S. (2021). Network Models and Sparse Graphon Estimation. In: 2021 Crimean Autumn Mathematical School-Symposium. Satera.

• 2020 - Present : Statistical Inference (Master in Data science and Business analytics ESSEC Business School France)
• 2019 - Present : Modelisation Statistiques (Grande Ecole - Master in Management ESSEC Business School France)
• 2017 - Present : Big Data Analytics (Master in Data science and Business analytics ESSEC Business School France)
• 2016 Mini-course: "Analyse des réseaux statistiques" ( Higher School of Economics (HSE) Russia)