5 citations · 5 across the 5 of their papers we have counts for
5 papers
Asymptotic Optimality of Thompson Sampling for Risk-Averse Bandits with Sub-Gaussian Rewards
Joel Q. L. Chang
We prove that , an anchor-free nonparametric Thompson Sampling algorithm for risk-averse bandits, achieves regret matching the instance-depend…
Reformulating - Limits in a Pedagogically Cleaner Way
Joel Q. L. Chang
We provide a simple reformulation of the - limit definition introduced in undergraduate calculus courses that enhances its pedagogical value for conceptual understanding and…
A Unifying Theory of Thompson Sampling for Continuous Risk-Averse Bandits
Joel Q. L. Chang, Vincent Y. F. Tan
This paper unifies the design and the analysis of risk-averse Thompson sampling algorithms for the multi-armed bandit problem for a class of risk functionals that are continuou…
Thompson Sampling for Gaussian Entropic Risk Bandits
Ming Liang Ang, Eloise Y. Y. Lim, Joel Q. L. Chang
The multi-armed bandit (MAB) problem is a ubiquitous decision-making problem that exemplifies exploration-exploitation tradeoff. Standard formulations exclude risk in decision maki…
Risk-Constrained Thompson Sampling for CVaR Bandits
Joel Q. L. Chang, Qiuyu Zhu, Vincent Y. F. Tan
The multi-armed bandit (MAB) problem is a ubiquitous decision-making problem that exemplifies the exploration-exploitation tradeoff. Standard formulations exclude risk in decision…