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cs.LG2026
Multi-agent imitation learning with function approximation: Linear Markov games and beyond
Luca Viano, Till Freihaut, Emanuele Nevali +3
In this work, we present the first theoretical analysis of multi-agent imitation learning (MAIL) in linear Markov games where both the transition dynamics and each agent's reward f…
cs.LG2026
Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution
Adam Barla, Emanuele Nevali, Luca Viano +1
We introduce PEPO (Pessimistic Ensemble based Preference Optimization), a single-step Direct Preference Optimization (DPO)-like algorithm to mitigate the well-known over-optimizati…
cs.LG2025
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…