I am a final year PhD student at INRIA Thoth under the supervision of Julien Mairal and Pierre Gaillard. I also belong to the causal inference research group in 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

• May 2023: SCRM paper is accepted to ICML23!
• July 2022: I am attending ICML22 and will be presenting a poster, see you in Baltimore!
• July 2022: I am attending the EEML summer school in Lithuania, see you in Vilnius!

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]