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Pages
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Posts
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portfolio
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publications
Max K-armed bandit: on the ExtremeHunter algorithm and beyond
Published in ECML PKDD 2017, Skopje, Macedonia, 2017
co-authors: S. Clémençon, A. Garivier, A. Sabourin, C. Vernade
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Ranking data with continuous labels through oriented recursive partitions
Published in NeurIPS 2017, Long Beach, USA, 2017
co-author: S. Clémençon
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Profitable bandits
Published in ACML 2018, Beijing, China, 2018
co-authors: S. Clémençon, A. Garivier
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Dimensionality reduction and (bucket) ranking: a mass transportation approach
Published in ALT 2019, Chicago, USA, 2019
co-authors: A. Korba, S. Clémençon
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Weighted empirical risk minimization: sample selection bias correction based on importance sampling
Published in ICMA 2020, 2020
co-authors: R. Vogel, S. Clémençon, C. Tillier
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[PhD thesis] Ranking and risk-aware reinforcement learning
Published in Institut polytechnique de Paris, 2020
supervisors: Stephan Clémençon, Aurélien Garivier and Anne Sabourin
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Robustness and risk management via distributional dynamic programming
Published in preprint, 2021
co-author: Gergely Neu
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Checkered Regression
Published in preprint, 2022
Abstract. This paper introduces the checkered regression model, a nonlinear generalization of logistic regression. More precisely, this new binary classifier relies on the multivariate function $\frac{1}{2}\left( 1 + \tanh(\frac{z_1}{2})\times\dots\times\tanh(\frac{z_m}{2}) \right)$, which coincides with the usual sigmoid function in the univariate case $m=1$. While the decision boundary of logistic regression consists of a single hyperplane, our method is shown to tessellate the feature space by any given number $m\ge 1$ of hyperplanes. In order to fit the model’s parameters to some labeled data, we describe a classic empirical risk minimization framework based on the cross entropy loss. A multiclass version of our approach is also proposed.
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Distributional deep Q-learning with CVaR regression
Published in Deep Reinforcement Learning Workshop at NeurIPS 2022, 2022
co-authors: R. Alami, Y.A. Dahou Djilali, K. Fedyanin, E. Moulines, M. Panov
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A Nested Matrix-Tensor Model for Noisy Multi-view Clustering
Published in preprint, 2023
co-authors: M.E.A. Seddik, H. Goulart, M. Debbah
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One-Step Distributional Reinforcement Learning
Published in Transactions on Machine Learning Research, 2023
co-authors: R. Alami, Y.A. Dahou Djilali, K. Fedyanin, E. Moulines
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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