Learning Determinantal Point Processes

Learning Non Symmetric Determinantal Point Processes, Joint with M. Gartrell, E. Dohmatob and S. Krichene, Submitted (arXiv1905.12962)

Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem, Accepted at NIPS 2018 (arXiv:1811.00465; For the presentation: Poster)

Maximum likelihood estimation of Determinantal Point Processes, Joint with A. Moitra, P. Rigollet and J. Urschel, Submitted (arXiv:1701.06501)

Learning Determinantal Point Processes with Moments and Cycles, Joint with A. Moitra, P. Rigollet and J. Urschel, Accepted at ICML 2017 (For the presentation: Slides – Poster)

Rates of estimation for determinantal point processes, Joint with A. Moitra, P. Rigollet and J. Urschel, Accepted at COLT 2017 (For the presentation: Slides – Poster)

Set estimation / Stochastic geometry

Adaptive estimation of convex polytopes and convex sets from noisy data, Electronic Journal of Statistics, Vol. 7, pp. 1301-1327 (2013)

Adaptive estimation of polytopal and convex support, Probability Theory and Related Fields, Vol. 164, pp. 1-16 (2016)

A universal deviation inequality for random polytopes, Working paper (arXiv:1311.2902)

A change-point problem and inference for segment signals, ESAIM: Probability and Statistics, Vol. 22, pp. 210-235 (2018)

Uniform behaviors of random polytopes under the Hausdorff metric, Bernoulli, Vol. 25, pp. 1770-1793 (2019)

Concentration of the empirical level sets of Tukey’s halfspace depth, Probability Theory and Related Fields, Vol. 173, pp. 1165-1196 (2019)

Uniform deviation and moment inequalities for random polytopes with general densities in arbitrary convex bodies, Submitted (arXiv:1704.01620)

Estimation of convex supports from noisy measurements, Joint with J. Klusowski and X. Yang, Submitted (arXiv:1804.09879)

Methods for Estimation of Convex Sets, Statistical Science, Vol. 33, pp. 615-632 (2018)


Best Arm identification for Contaminated Bandits, Joint with J. Altschuler and A. Malek, Accepted for publication at Journal of Machine Learning Research (arXiv:1802.09514)

A nonasymptotic law of iterated logarithm for robust online estimators, Joint with A. Dalalyan and N. Schreuder, Submitted (arXiv:1903.06576)


Differentially Private Sub-Gaussian Location Estimators, Joint with M. Avella, Submitted

Learning rates for Gaussian mixtures under group invarianceAccepted at the Conference On Learning Theory (COLT) 2019 (arXiv:1902.11176)

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