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stat.ML2025
Implicit Updates for Average-Reward Temporal Difference Learning
Hwanwoo Kim, Dongkyu Derek Cho, Eric Laber
Temporal difference (TD) learning is a cornerstone of reinforcement learning. In the average-reward setting, standard TD() is highly sensitive to the choice of step-size and thu…
stat.ML2025
Exploiting Concavity Information in Gaussian Process Contextual Bandit Optimization
Kevin Li, Eric Laber
The contextual bandit models sequential decision-making problems in which rewards depend on both the chosen action and observed context. In many domains such as medicine, business,…
stat.ML2025
Empirical Bound Information-Directed Sampling for Norm-Agnostic Bandits
Piotr M. Suder, Eric Laber
Information-directed sampling (IDS) is a powerful framework for solving bandit problems which has shown strong results in both Bayesian and frequentist settings. However, frequenti…