Batched Bandits

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8 papers0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Odds-Ratio Thompson Sampling: A Specification and Design Guide for Contrast-Based Multi-Armed Bandits
Sep 17, 2026Sulgi KimThompson Sampling Multi-Armed Bandits
Independent researcher
Dimension-Adaptive Batched Lipschitz Narrowing Without Knowing the Zooming Dimension
Sep 4, 2026Yasong FengLipschitz Bandits Batched Bandits
Memory--Batch Tradeoffs in Lipschitz Bandits
Aug 8, 2026Zicheng Lyu, Zengfeng HuangLipschitz Bandits Multi-Armed Bandits
Fudan University Shanghai, China · Fudan University Shanghai Innovation Institute Shanghai, China
Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates
May 31, 2026Sanghoon Yu, Min-hwan OhBatched Bandits Contextual Bandits
Seoul National University, Seoul, Republic of Korea
Batched Stochastic Linear Bandits with 1-Bit Communication Constraints
May 29, 2026Ivan Lau, Daniel McMorrow, Kevin Jamieson +1Multi-Armed Bandits Batched Bandits
National University of Singapore · University of Washington
Few Batches or Little Memory, But Not Both: Simultaneous Space and Adaptivity Constraints in Stochastic Bandits
Mar 14, 2026Ruiyuan Huang, Zicheng Lyu, Xiaoyi Zhu +1Batched Bandits Multi-Armed Bandits
School of Data Science, Fudan University · School of Data Science, Fudan University Shanghai Innovation Institute
Batched Bandits with Heavy-Tailed Rewards
Oct 4, 2025Yunwen Guo, Yunlun Shu, Gongyi Zhuo +1Lipschitz Bandits Multi-Armed Bandits
School of Mathematical Sciences, Fudan University, 220 Handan Rd., 200433, Shanghai, China · Shanghai Center for Mathematical Sciences, Fudan University, 220 Handan Rd., 200433, Shanghai, China
Stochastic Matching Bandits with Rare Optimization Updates
Sep 4, 2025Jung-hun Kim, Min-hwan OhBatched Bandits Multi-Armed Bandits
CREST, ESNAE Paris, FairPlay joint team · Seoul National University

Sep 17, 2026J/K move · Enter open · S save

Odds-Ratio Thompson Sampling: A Specification and Design Guide for Contrast-Based Multi-Armed Bandits

Sulgi Kim

Independent researcher

Thompson Sampling Multi-Armed Bandits Batched Bandits Non-Stationary Bandits A/b Testing

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Batched multi-armed bandits update on a service's own schedule, and the usual implementation carries each arm's absolute reward rate from one update to the next. When the shared level moves between batches, that memory goes stale even though the comparisons between arms may not have. Odds-Ratio Thompson Sampling (OR-TS) instead carries the joint posterior over log-odds contrasts and fits the common level afresh in every batch, marginalizing it out. This paper specifies that update, places it inside a Bayesian bandit agent with two controls, decay for how much past evidence survives an update and aggressiveness for how sharply belief becomes allocation, and evaluates it against absolute-rate memory. Across 86 public A/B series the level varies about twenty-five times more than the contrast. In prespecified synthetic environments a moving level costs absolute-rate memory five times the regret and leaves the best arm below a majority of traffic in 7 of 20 runs, against none for OR-TS. In a policy simulation built from 71 real experiments, where the contrasts are too small to resolve, expected-click differences stay within 0.1% for 58 of them, yet contrast memory still ends on the better arm more than twice as often. Where the contrasts themselves move, the bet fails, and that case is reported too.