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.
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.
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.
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.
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.
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.
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 • 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 • 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.
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.
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.
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.