Author

William Chang

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Preprint Aug 2026

Coordinating the Unknown Lipschitz Constant in Multiplayer Bandits

Motivated by decentralized applications, we study cooperative multi-agent bandits in continuous (Lipschitz) action spaces when the Lipschitz constant is unknown. We consider three information structures: (A)~unobserved actions with common rewards, (B)~observed actions with independent rewards, and (C)~unobserved action...

Ricardo Parada, Chenzhang Zhao, William Chang · 0 citations

#machine learning Preprint Sep 2026

Provably Efficient Reinforcement Learning in Continuous-Time Episodic MDPs with Poisson Decision Epochs

These results provide the first tight regret guarantees for Lipschitz-smooth continuous-time episodic MDPs with Poisson decision epochs.

Kenny Guo, Valentio Iverson, Sahan Wijetunga et al. · 0 citations

Conference Open access Jul 2026

DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clicks

In this work, we extend the Dependent Click Model (DCM) Bandits to a multiplayer information-asymmetric setting, where multiple agents interact with a shared ranked list and may observe multiple clicks per session, introducing new challenges for selection strategies. We study asymmetry in (1) actions and (2) rewards, p...

Andy Wang, Charlton Shih, William Chang · 2026 8th Asia Conference on... · 0 citations

Preprint Aug 2026

The Price of Decentralization in Top-$K$ Arm Identification

Change-of-measure lower bounds show that shared-reward identification is optimal up to one universal logarithmic factor, and that the entire statistical price of removing communication is a multiplicative $\rho^2$ in sample complexity.

Larissa Xu, Jasmine Nguyen, William Chang · 0 citations