153 citations · 427 across the 46 of their papers we have counts for
11 papers · 1 filter
Fair Active Learning: Solving the Labeling Problem in Insurance
Romuald Elie, Caroline Hillairet, François Hu +1
This paper addresses significant obstacles that arise from the widespread use of machine learning models in the insurance industry, with a specific focus on promoting fairness. The…
Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO
Paul Muller, Mark Rowland, Romuald Elie +6
Recent advances in multiagent learning have seen the introduction ofa family of algorithms that revolve around the population-based trainingmethod PSRO, showing convergence to Nash…
Solving N-player dynamic routing games with congestion: a mean field approach
Theophile Cabannes, Mathieu Lauriere, Julien Perolat +7
The recent emergence of navigational tools has changed traffic patterns and has now enabled new types of congestion-aware routing control like dynamic road pricing. Using the funda…
Generalization in Mean Field Games by Learning Master Policies
Sarah Perrin, Mathieu Laurière, Julien Pérolat +3
Mean Field Games (MFGs) can potentially scale multi-agent systems to extremely large populations of agents. Yet, most of the literature assumes a single initial distribution for th…
Fairness guarantee in multi-class classification
Christophe Denis, Romuald Elie, Mohamed Hebiri +1
Algorithmic Fairness is an established area of machine learning, willing to reduce the influence of hidden bias in the data. Yet, despite its wide range of applications, very few w…
Evolutionary Dynamics and -Regret Minimization in Games
Georgios Piliouras, Mark Rowland, Shayegan Omidshafiei +4
Regret has been established as a foundational concept in online learning, and likewise has important applications in the analysis of learning dynamics in games. Regret quantifies t…