Author

William Chang

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Preprint Aug 2026

Robust Multi-Agent Bandits with Heavy-Tailed Rewards and Information Asymmetry

The multi-armed bandit problem is a central framework in sequential decision-making, extensively studied under sub-Gaussian reward assumptions. However, real-world applications often involve heavy-tailed reward distributions and decentralized, information-asymmetric interactions. We study multi-agent multi-armed bandit...

Daphne Feng, Ricardo Parada, Lily Jiang et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Improved Multiplayer Bandit Algorithm for Bernoulli Rewards

We study the multiplayer multi-armed bandit problem with information asymmetry under Bernoulli rewards, for three information structures: asymmetry in actions, in rewards, and in both. Replacing the Hoeffding-style confidence intervals of prior work with Kullback--Leibler (KL) divergence-based bounds gives strictly tig...

K. Nguyen, Ricardo Parada, William Chang · 0 citations