3 papers
cs.LG2026
PAC-Bayesian Reward-Certified Outcome Weighted Learning
Yuya Ishikawa, Shu Tamano
Estimating optimal individualized treatment rules (ITRs) via outcome weighted learning (OWL) often relies on observed rewards that are noisy or optimistic proxies for the true late…
stat.ME2026
Error-Controlled Borrowing from External Data Using Wasserstein Ambiguity Sets
Yui Kimura, Shu Tamano
Incorporating external data can improve the efficiency of clinical trials, but distributional mismatches between current and external populations threaten the validity of inference…
stat.ML2026
DOLCE: Decomposing Off-Policy Evaluation/Learning into Lagged and Current Effects
Shu Tamano
Off-policy evaluation and learning in contextual bandits use logged interaction data to estimate and optimize the value of a target policy. Most existing methods require sufficient…