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ensemble sampling

topic1 events
papersTODAY 04:00 UTC

Linear Ensemble Sampling Retains Regret Guarantees With Smaller Ensembles

A new arXiv paper examines how few models are needed in ensemble sampling, a randomized-exploration method for sequential decision problems, while still preserving theoretical regret bounds. Prior results relied on ensembles larger than practical implementations typically use, leaving the minimum viable size unclear. The work analyzes this setting for linear models.

research/reinforcement-learning/bandits/ensembles