1 citations · 1 across the 3 of their papers we have counts for
3 papers
stat.ML2024
Thompson Sampling in Partially Observable Contextual Bandits
Hongju Park, Mohamad Kazem Shirani Faradonbeh
Contextual bandits constitute a classical framework for decision-making under uncertainty. In this setting, the goal is to learn the arms of highest reward subject to contextual in…
stat.ML2022★ 1 cited
Worst-case Performance of Greedy Policies in Bandits with Imperfect Context Observations
Hongju Park, Mohamad Kazem Shirani Faradonbeh
Contextual bandits are canonical models for sequential decision-making under uncertainty in environments with time-varying components. In this setting, the expected reward of each…
stat.ML2022
Efficient Algorithms for Learning to Control Bandits with Unobserved Contexts
Hongju Park, Mohamad Kazem Shirani Faradonbeh
Contextual bandits are widely-used in the study of learning-based control policies for finite action spaces. While the problem is well-studied for bandits with perfectly observed c…