I am a Research Fellow at the Gatsby Computational Neuroscience Unit at University College London working under the supervision of Arthur Gretton.

Prior to that, I was a postdoc at INRIA MIND working with Bertrand Thirion, Judith Abecassis, and Julie Josse in 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 at the interface of counterfactual reasoning and online learning algorithms.

Prior to that, I received an M.Eng. degree in applied mathematics and computer science from CentraleSupélec 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

• Oct 2025: I am attending the MML workshop in Ascona, Switzerland!
• Sept 2025: Two papers accepted at NeurIPS!
• Jan 2025: I joined the Gatsby Unit!

Publications

Journal papers

• Causal mediation analysis with one or multiple mediators: a comparative study
  Judith Abecassis, Houssam Zenati, Sami Boumaïza, Julie Josse, Bertrand Thirion.
  Psychological Methods, 2025.
  [Paper]

• Counterfactual Learning of Stochastic Policies with Continuous Actions
  Houssam Zenati, Alberto Bietti, Matthieu Martin, Eustache Diemert, Pierre Gaillard, Julien Mairal.
  Transactions on Machine Learning Research, 2025.
  [Paper] · [Code]

Working papers

• Kernel Treatment Effects with Adaptively Collected Data
  Houssam Zenati, Bariscan Bozkurt, Arthur Gretton.
  [Paper] · [Code]

• Online Policy Learning via a Self-Normalized Maximal Inequality
  Samuel Girard, Aurelien Bibaut, Houssam Zenati.
  

Conference papers

• Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings
  Houssam Zenati, Bariscan Bozkurt, Arthur Gretton.
  Neural Information Processing Systems (NeurIPS), 2025.
  [Paper] · [Code]

• Density Ratio-Free Doubly Robust Proxy Causal Learning
  Bariscan Bozkurt, Houssam Zenati, Dimitri Meunier, Liyuan Xu, Arthur Gretton.
  Neural Information Processing Systems (NeurIPS), 2025.
  [Paper] · [Code]

• Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments
  Houssam Zenati, Judith Abecassis, Julie Josse, Bertrand Thirion.
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.
  [Paper] · [Code]

• Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits
  Julien Zhou, Thibaud Rahier, Julyan Arbel, Houssam Zenati, Pierre Gaillard.
  Neural Information Processing Systems (NeurIPS), 2024.
  [Paper]

• Sequential Counterfactual Risk Minimization
  Houssam Zenati, Eustache Diemert, Matthieu Martin, Julien Mairal, Pierre Gaillard.
  International Conference on Machine Learning (ICML), 2023.
  [Paper] · [Code]

• Nested Bandits
  Matthieu Martin, Panagiotis Mertikopoulos, Thibaud Rahier, Houssam Zenati.
  International Conference on Machine Learning (ICML), 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 (AISTATS), 2022.
  [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, Georgios Piliouras.
  International Conference on Learning Representations (ICLR), 2019.
  [Paper]

• Adversarially Learned Anomaly Detection
  Houssam Zenati, Manon Romain, Chuan-Sheng Foo, Bruno Lecouat, Vijay Chandrasekhar.
  IEEE International Conference on Data Mining (ICDM), 2019.
  [Paper] · [Code]

Workshop papers

• Optimization Approaches for Counterfactual Risk Minimization with Continuous Actions
  Houssam Zenati, Alberto Bietti, Matthieu Martin, Eustache Diemert, Julien Mairal.
  ICLR Workshop: Causal Learning for Decision Making, 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.
  MICCAI 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, Pavitra Krishnaswamy.
  NeurIPS ML4H Workshop, 2018.
  [Paper]

• Semi-Supervised Learning with GANs: Revisiting Manifold Regularization
  Bruno Lecouat, Chuan-Sheng Foo, Houssam Zenati, Vijay Chandrasekhar.
  ICLR Workshop, 2018.
  [Paper] · [Code]

• Efficient GAN-Based Anomaly Detection
  Houssam Zenati, Bruno Lecouat, Chuan-Sheng Foo, Gaurav Manek, Vijay Chandrasekhar. ICLR Workshop (submitted), 2018.
  [Paper] · [Code]