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Found 5 papers, 1 papers with code
Designing Transportable Experiments
To reduce the variance of our estimator, we design a covariate balance condition (Target Balance) between the treatment and control groups based on the target population.
Methodology
Regret Balancing for Bandit and RL Model Selection
Given a set of base learning algorithms, an effective model selection strategy adapts to the best learning algorithm in an online fashion.
Model Selection in Contextual Stochastic Bandit Problems
Our methods rely on a novel and generic smoothing transformation for bandit algorithms that permits us to obtain optimal $O(\sqrt{T})$ model selection guarantees for stochastic contextual bandit problems as long as the optimal base algorithm satisfies a high probability regret guarantee.
Thompson Sampling and Approximate Inference
We study the effects of approximate inference on the performance of Thompson sampling in the $k$-armed bandit problems.
Thompson Sampling with Approximate Inference
We study the effects of approximate inference on the performance of Thompson sampling in the $k$-armed bandit problems.
