William Chang — accepted AI-conference papers

2024

Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs

Tianyuan Jin, Hao-Lun Hsu, William Chang, Pan Xu

AAAI 2024technical

We study the multi-agent multi-armed bandit (MAMAB) problem, where agents are factored into overlapping groups. Each group represents a hyperedge, forming a hypergraph over the agents. At each round of interaction, the learner pulls a joint arm (composed of individual arms for each agent) and receiv…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2023

No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions

Tiancheng Jin, Junyan Liu, Chloé Rouyer, William Chang, Chen-Yu Wei, Haipeng Luo

NeurIPS 2023poster

Existing online learning algorithms for adversarial Markov Decision Processes achieve $\mathcal{O}(\sqrt{T})$ regret after $T$ rounds of interactions even if the loss functions are chosen arbitrarily by an adversary, with the caveat that the transition function has to be fixed. This is because it h…

Cited by 17 Source ↗PDF ↗Scholar ↗