Houssam Zenati

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Personal webpage.

GitHub / Google Scholar

I am postdoc at the INRIA Mind and INRIA Soda teams working with Bertrand Thirion, Judith Abecassis and Julie Josse at the INRIA PreMedical team.

My thesis manuscript was on Efficient methods in counterfactual policy learning and sequential decision making.

I did my PhD at INRIA Thoth under the supervision of Julien Mairal and Pierre Gaillard in collaboration with the causal inference research group at the Criteo AI Lab. My research interests lie in the interface of counterfactual reasoning and online learning algorithms.

Prior to that, I received a M.Eng. degree in applied mathematics and computer science from CentraleSupelec as well as the MVA M.Sc. degree from ENS Paris-Saclay.

Previously, I also worked at the Institute for Infocomm Research under the supervision of Chuan-Sheng Foo and Vijay Chandrasekhar.

You can find my CV here.

I also like hiking, traveling and learning Japanese, as you may see here.

News

Publications

Conference papers

Sequential Counterfactual Risk Minimization
Houssam Zenati, Eustache Diemert, Matthieu Martin, Julien Mairal, Pierre Gaillard
International Conference on Machine Learning, 2023

[Paper] [Code]

Nested Bandits
Matthieu Martin, Panagiotis Mertikopoulos, Thibaud Rahier, Houssam Zenati
International Conference on Machine Learning, 2022

[Paper] [Code]

Efficient Kernelized UCB for Contextual Bandits
Houssam Zenati, Alberto Bietti, Eustache Diemert, Julien Mairal, Matthieu Martin, Pierre Gaillard
International Conference on Artificial Intelligence and Statistics, 2022

[Paper] [Code]

Counterfactual Learning of Stochastic Policies with Continuous Actions: from Models to Offline Evaluation
Houssam Zenati, Alberto Bietti, Matthieu Martin, Eustache Diemert, Pierre Gaillard, Julien Mairal

[Paper] [Code]

Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
Panagiotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar and Georgios Piliouras
International Conference on Learning Representation, 2019

[Paper]

Adversarially Learned Anomaly Detection
Houssam Zenati, Manon Romain, Chuan-Sheng Foo, Bruno Lecouat, Vijay Chandrasekhar
International Conference on Data Mining, 2019

[Paper] [Code]

Workshop papers

Optimization approaches for counterfactual risk minimization with continuous actions
Houssam Zenati, Alberto Bietti, Matthieu Martin, Eustache Diemert, Julien Mairal
International Conference on Learning Representation, Causal Learning for Decision Making Workshop, 2020

[Paper] [Code]

Towards practical unsupervised anomaly detection on retinal images
Khalil Ouardini, Huijuan Yang, Balagopal Unnikrishnan, Manon Romain, Camille Garcin, Houssam Zenati, J Peter Campbell, Michael F Chiang, Jayashree Kalpathy-Cramer, Vijay Chandrasekhar, Pavitra Krishnaswamy, Chuan-Sheng Foo.
International Conference on Medical Image Computing and Computer Assisted Intervention Workshop, 2019

[Paper]

Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
Bruno Lecouat, Ken Chang, Chuan-Sheng Foo, Balagopal Unnikrishnan, James Brown, Houssam Zenati, Andrew Beers, Vijay Chandrasekhar, Jayashree Kalpathy-Cramer and Pavitra Krishnaswamy.
Neural Information Processing Systems, ML4H Workshop 2018

[Paper]

Semi-Supervised Learning With GANs: Revisiting Manifold Regularization
Bruno Lecouat, Chuan Sheng Foo, Houssam Zenati, Vijay Chandrasekhar
International Conference on Learning Representation Workshop, 2018

[Paper] [Code]

Efficient GAN Based Anomaly Detection
Houssam Zenati, Bruno Lecouat, Chuan Sheng Foo, Gaurav Manek, Vijay Chandrasekhar

[Paper] [Code]