482 citations · 677 across the 19 of their papers we have counts for
22 papers
Nonparametric Instrumental Variable Analysis Without Structural Equations: Debiased Inference on Functionals of Inverse Problems with No Solutions
Zikai Shen, Nathan Kallus, Dimitri Meunier +3
We consider debiased inference on finite-dimensional functionals of infinite-dimensional least-squares solutions to inverse problems as a way to avoid having to assume exact soluti…
Doubly Robust Proxy Causal Learning with Neural Mean Embeddings
Bariscan Bozkurt, Alexandre Galashov, Dimitri Meunier +3
We propose a novel doubly robust neural mean-embedding framework for proxy causal learning (PCL) for continuous and structured treatments. PCL uses proxy variables and bridge funct…
Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
Houssam Zenati, Arthur Gretton
Distributional treatment effects can be invisible to means: a treatment may preserve average outcomes while changing tails, modes, dispersion, or rare-event probabilities. Kernel t…
Functional Natural Policy Gradients
Aurelien Bibaut, Houssam Zenati, Thibaud Rahier +1
We propose a cross-fitted debiasing device for policy learning from offline data. A key consequence of the resulting learning principle is regret even for policy classes…
Efficient Inference after Directionally Stable Adaptive Experiments
Zikai Shen, Houssam Zenati, Nathan Kallus +3
We study inference on scalar-valued pathwise differentiable targets after adaptive data collection, such as a bandit algorithm. We introduce a novel target-specific condition, dire…
Fast Best-in-Class Regret for Contextual Bandits
Samuel Girard, Aurelien Bibaut, Arthur Gretton +2
We study the problem of stochastic contextual bandits in the agnostic setting, where the goal is to compete with the best policy in a given class without assuming realizability or…
Kernel Treatment Effects with Adaptively Collected Data
Houssam Zenati, Bariscan Bozkurt, Arthur Gretton
Adaptive experiments improve efficiency by adjusting treatment assignments based on past outcomes, but this adaptivity breaks the i.i.d.\ assumptions that underpin classical asympt…
Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings
Houssam Zenati, Bariscan Bozkurt, Arthur Gretton
Estimating the distribution of outcomes under counterfactual policies is critical for decision-making in domains such as recommendation, advertising, and healthcare. We propose and…
Causal mediation analysis with one or multiple mediators: a comparative study
Judith Abécassis, Houssam Zenati, Sami Boumaïza +2
Mediation analysis breaks down the causal effect of a treatment on an outcome into an indirect effect, acting through a third group of variables called mediators, and a direct effe…
Density Ratio-Free Doubly Robust Proxy Causal Learning
Bariscan Bozkurt, Houssam Zenati, Dimitri Meunier +2
We study the problem of causal function estimation in the Proxy Causal Learning (PCL) framework, where confounders are not observed but proxies for the confounders are available. T…
Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments
Houssam Zenati, Judith Abécassis, Julie Josse +1
Uncovering causal mediation effects is of significant value to practitioners seeking to isolate the direct treatment effect from the potential mediated effect. We propose a double…
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits
Julien Zhou, Pierre Gaillard, Thibaud Rahier +2
We address the problem of stochastic combinatorial semi-bandits, where a player selects among P actions from the power set of a set containing d base items. Adaptivity to the probl…
Sequential Counterfactual Risk Minimization
Houssam Zenati, Eustache Diemert, Matthieu Martin +2
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. In this…
Nested bandits
Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier +1
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…
Efficient Kernel UCB for Contextual Bandits
Houssam Zenati, Alberto Bietti, Eustache Diemert +3
In this paper, we tackle the computational efficiency of kernelized UCB algorithms in contextual bandits. While standard methods require a O(CT^3) complexity where T is the horizon…
Counterfactual Learning of Stochastic Policies with Continuous Actions
Houssam Zenati, Alberto Bietti, Matthieu Martin +3
Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare. In this paper, we address the problem of le…
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
Bruno Lecouat, Ken Chang, Chuan-Sheng Foo +7
Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks. But these methods rely on large labeled datasets that require…
Adversarially Learned Anomaly Detection
Houssam Zenati, Manon Romain, Chuan Sheng Foo +2
Anomaly detection is a significant and hence well-studied problem. However, developing effective anomaly detection methods for complex and high-dimensional data remains a challenge…
Manifold regularization with GANs for semi-supervised learning
Bruno Lecouat, Chuan-Sheng Foo, Houssam Zenati +1
Generative Adversarial Networks are powerful generative models that are able to model the manifold of natural images. We leverage this property to perform manifold regularization b…
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati +3
Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond. By necess…
Semi-Supervised Learning with GANs: Revisiting Manifold Regularization
Bruno Lecouat, Chuan-Sheng Foo, Houssam Zenati +1
GANS are powerful generative models that are able to model the manifold of natural images. We leverage this property to perform manifold regularization by approximating the Laplaci…
Efficient GAN-Based Anomaly Detection
Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat +2
Generative adversarial networks (GANs) are able to model the complex highdimensional distributions of real-world data, which suggests they could be effective for anomaly detection.…