Researcher in Game Theory and Sequential Learning

Il y a 2 mois

Palaiseau, Île-de-France INRIA Temps plein
About the Project

The proposed research project lies at the intersection of repeated games and statistical learning, with the aim of developing robust and equitable algorithms.

Many learning algorithms operate sequentially (robotics, conversational agents, human-computer interaction applications, etc.). Game theory provides a framework for analyzing interactions, from which simple and robust algorithms can be derived, particularly in a multi-agent context. It is then possible to provide algorithms with theoretical guarantees that ensure the correct functioning of the implemented algorithms in practice.

Repeated game theory dates back to the mid-20th century and is particularly suitable for the study of sequential learning. Thus, asynchronous repeated games are a direct extension; similarly, for the theory of approachability, and matching algorithms. The tools developed will be brought into contact with other areas of mathematics to answer new questions. In particular, we will focus on developing equitable and/or robust algorithms against strategic manipulations for various applications, including cognitive telecommunications networks, matching algorithms, and prediction algorithms.

Research Objectives

We will consider three largely independent problems.

  1. Ax 1: We will aim to develop the theory of asynchronous continuous-time games in order to obtain simple and robust algorithms, directly applicable to the problem of multi-player bandits. Motivated by telecommunications networks, the problem of multi-player bandits has recently developed significantly. However, the various proposed solutions have difficulty taking into account the asynchronicity of the devices used in practice. Through a new mathematical formulation of asynchronicity, we aim to develop simple and robust algorithms for this problem.
  2. Ax 2: Repeated game theory is also a relevant framework for extending the theory of fair learning. On the one hand, we will focus on Blackwell's approachability theory to develop sequential prediction algorithms with fairness guarantees. Recent works by Chzhen, Giraud, and Stoltz have already allowed us to advance in this direction. The goal of this axis would be to extend existing results to stronger notions of fairness, which requires revisiting Blackwell's approachability method.
  3. Ax 3: On the other hand, we will study sequential learning algorithms for matching problems, and in particular in the context of fair matching. Sequential learning of matching has developed significantly in recent years. However, these recent works have not been interested in the notion of fairness in matching, which is an important notion and considered in the economic literature (without learning). The goal of this axis would be to link these two areas of the literature: sequential learning on matching problems and fair matching.
Requirements

We are looking for a researcher with expertise in game theory, sequential learning, and a Master's degree in mathematics or applied mathematics.

Benefits

We offer a range of benefits, including:

  • Subsidized meals
  • Partially reimbursed public transportation
  • 7 weeks of annual leave + 10 days of RTT (full-time basis) + possibility of exceptional leave authorization
  • Opportunity for telecommuting and flexible working hours
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
  • Social, cultural, and sports benefits (Association for the Management of Social Works of Inria)
  • Access to professional training
Salary

€2,100 gross per month

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