Thompson Sampling viewed as online optimization

A new paper recasts Thompson Sampling, a widely used bandit algorithm, as an online optimization problem. This perspective reveals how posterior sampling balances exploration and exploitation by mimicking a Bellman-optimal policy, regularized by residual uncertainty. The research offers a deeper understanding of Thompson Sampling's dynamics and a method for policy improvement. AI

IMPACT Provides a new theoretical framework for understanding and potentially improving bandit algorithms used in AI.

RANK_REASON Academic paper on a machine learning algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Thompson Sampling
• Yanlin Qu

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →