no code implementations • 23 Feb 2024 • Julien Zhou, Pierre Gaillard, Thibaud Rahier, Houssam Zenati, Julyan Arbel
We address the problem of stochastic combinatorial semi-bandits, where a player can select from P subsets of a set containing d base items.
1 code implementation • 23 Feb 2023 • Houssam Zenati, Eustache Diemert, Matthieu Martin, Julien Mairal, Pierre Gaillard
Counterfactual Risk Minimization (CRM) is a framework for dealing with the logged bandit feedback problem, where the goal is to improve a logging policy using offline data.
no code implementations • 19 Jun 2022 • Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier, Houssam Zenati
In many online decision processes, the optimizing agent is called to choose between large numbers of alternatives with many inherent similarities; in turn, these similarities imply closely correlated losses that may confound standard discrete choice models and bandit algorithms.
1 code implementation • 11 Feb 2022 • Houssam Zenati, Alberto Bietti, Eustache Diemert, Julien Mairal, Matthieu Martin, Pierre Gaillard
While standard methods require a O(CT^3) complexity where T is the horizon and the constant C is related to optimizing the UCB rule, we propose an efficient contextual algorithm for large-scale problems.
1 code implementation • 22 Apr 2020 • Houssam Zenati, Alberto Bietti, Matthieu Martin, Eustache Diemert, Pierre Gaillard, Julien Mairal
Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare.
no code implementations • ICLR 2019 • Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras
Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond.
1 code implementation • 19 Dec 2018 • Bruno Lecouat, Ken Chang, Chuan-Sheng Foo, Balagopal Unnikrishnan, James M. Brown, Houssam Zenati, Andrew Beers, Vijay Chandrasekhar, Jayashree Kalpathy-Cramer, Pavitra Krishnaswamy
Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks.
4 code implementations • 6 Dec 2018 • Houssam Zenati, Manon Romain, Chuan Sheng Foo, Bruno Lecouat, Vijay Ramaseshan Chandrasekhar
Anomaly detection is a significant and hence well-studied problem.
1 code implementation • ICLR 2019 • Bruno Lecouat, Chuan-Sheng Foo, Houssam Zenati, Vijay Chandrasekhar
Generative Adversarial Networks are powerful generative models that are able to model the manifold of natural images.
no code implementations • 7 Jul 2018 • Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras
Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond.
2 code implementations • 23 May 2018 • Bruno Lecouat, Chuan-Sheng Foo, Houssam Zenati, Vijay R. Chandrasekhar
GANS are powerful generative models that are able to model the manifold of natural images.
7 code implementations • 17 Feb 2018 • Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, Vijay Ramaseshan Chandrasekhar
However, few works have explored the use of GANs for the anomaly detection task.