Summary of Lc-tsallis-inf: Generalized Best-of-both-worlds Linear Contextual Bandits, by Masahiro Kato and Shinji Ito
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits
by Masahiro Kato, Shinji Ito
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 proposes an algorithm for the linear contextual bandit problem, which has a regret of O(log(T)) in both stochastic and adversarial regimes. The existing Best-of-Both-Worlds (BoBW) algorithms have a regret of O(log^2(T)), but with a suboptimality gap lower-bounded by a positive constant. The proposed algorithm relaxes this assumption and achieves the same regret bound without it. Additionally, the paper introduces a margin condition that characterizes the problem difficulty linked to the suboptimality gap using a parameter β. The algorithm is based on Follow-The-Regularized-Leader with Tsallis entropy and referred to as α-Linear-Contextual (LC)-Tsallis-INF. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us solve a tricky math problem called the linear contextual bandit problem. This problem involves making good choices when we don’t know everything, which is important in many areas like medicine or finance. The paper proposes a new way to make these choices that’s faster and better than existing methods. It does this by relaxing some assumptions and introducing a new condition that helps us understand how hard the problem is. |
