5 citations · 11 across the 10 of their papers we have counts for
10 papers
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…
Zeroth-order non-convex learning via hierarchical dual averaging
Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos +1
We propose a hierarchical version of dual averaging for zeroth-order online non-convex optimization - i.e., learning processes where, at each stage, the optimizer is facing an unkn…
About evaluation metrics for contextual uplift modeling
Christophe Renaudin, Matthieu Martin
In this tech report we discuss the evaluation problem of contextual uplift modeling from the causal inference point of view. More particularly, we instantiate the individual treatm…
Online non-convex optimization with imperfect feedback
Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos +1
We consider the problem of online learning with non-convex losses. In terms of feedback, we assume that the learner observes - or otherwise constructs - an inexact model for the lo…
Individual Treatment Prescription Effect Estimation in a Low Compliance Setting
Thibaud Rahier, Amélie Héliou, Matthieu Martin +2
Individual Treatment Effect (ITE) estimation is an extensively researched problem, with applications in various domains. We model the case where there exists heterogeneous non-comp…
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…
A Multilevel Stochastic Gradient method for PDE-constrained Optimal Control Problems with uncertain parameters
Matthieu Martin, Fabio Nobile, Panagiotis Tsilifis
In this paper, we present a multilevel Monte Carlo (MLMC) version of the Stochastic Gradient (SG) method for optimization under uncertainty, in order to tackle Optimal Control Prob…
PDE-constrained optimal control problems with uncertain parameters using SAGA
Matthieu C. Martin, Fabio Nobile
We consider an optimal control problem (OCP) for a partial differential equation (PDE) with random coefficients. The optimal control function is a deterministic, distributed forcin…