1 citations · 1 across the 4 of their papers we have counts for
3 papers · 1 filter
Rate optimal learning of equilibria from data
Till Freihaut, Luca Viano, Emanuele Nevali +3
We close open theoretical gaps in Multi-Agent Imitation Learning (MAIL) by characterizing the limits of non-interactive MAIL and presenting the first interactive algorithm with nea…
Learning Equilibria from Data: Provably Efficient Multi-Agent Imitation Learning
Till Freihaut, Luca Viano, Volkan Cevher +2
This paper provides the first expert sample complexity characterization for learning a Nash equilibrium from expert data in Markov Games. We show that a new quantity named the sing…
Clustered KL-barycenter design for policy evaluation
Simon Weissmann, Till Freihaut, Claire Vernade +2
In the context of stochastic bandit models, this article examines how to design sample-efficient behavior policies for the importance sampling evaluation of multiple target policie…