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Bary Pradelski

2 accepted papers

2024

A Geometric Decomposition of Finite Games: Convergence vs. Recurrence under Exponential Weights

ICML 2024spotlight

In view of the complexity of the dynamics of learning in games, we seek to decompose a game into simpler components where the dynamics' long-run behavior is well understood. A natural starting point for this is Helmholtz's theorem, which decomposes a vector field into a potential and an incompressib…

Cited by 10SourcePDFScholar
2024

No-regret Learning in Harmonic Games: Extrapolation in the Face of Conflicting Interests

NeurIPS 2024spotlight

The long-run behavior of multi-agent online learning -- and, in particular, no-regret learning -- is relatively well-understood in potential games, where players have common interests. By contrast, in general harmonic games -- the strategic complement of potential games, where players have competing…

Cited by 2SourcePDFScholar