Summary of Effective Off-policy Evaluation and Learning in Contextual Combinatorial Bandits, by Tatsuhiro Shimizu et al.
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits
by Tatsuhiro Shimizu, Koichi Tanaka, Ren Kishimoto, Haruka Kiyohara, Masahiro Nomura, Yuta Saito
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores off-policy evaluation and learning (OPE/L) in contextual combinatorial bandits (CCB), a problem that selects a subset from available options, such as furniture pieces for interior design sales. This setting is common in recommender systems and healthcare, but OPE/L of CCB remains unexplored. Typical methods like regression and importance sampling can be applied, but they face challenges due to high bias or variance caused by the exponential growth in available subsets. The paper introduces a factored action space that decomposes each subset into binary indicators, allowing for more effective OPE. An estimator called OPCB uses importance sampling-based approach for unbiased main effect estimation and regression-based approach for low-variance residual effect estimation. OPCB achieves substantial variance reduction compared to conventional methods and bias reduction relative to regression methods under certain conditions. Theoretical analysis and experiments demonstrate OPCB’s superior performance in both OPE and OPL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to learn from experience and make good decisions, even when the rules change. Imagine you’re designing a room and want to choose the right furniture pieces. You might select a bed and a drawer from many options, like beds, drawers, chairs, etc. This problem is common in areas like recommending products or healthcare. The paper explores how to evaluate and learn from this process without following the rules all the time. It proposes a new way of thinking about the problem that breaks it down into smaller parts, making it easier to make good decisions. The results show that this approach works better than other methods in certain situations. |
Keywords
» Artificial intelligence » Regression
