Summary of Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring, By Taira Tsuchiya et al.
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring
by Taira Tsuchiya, Shinji Ito, Junya Honda
First submitted to arxiv on: 13 Feb 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 Partial monitoring is a framework for online decision-making problems with limited feedback. The exploration-by-optimization (ExO) approach has been shown to achieve optimal bounds in adversarial environments, but its application in stochastic environments degrades regret bounds significantly. To address this issue, we propose a hybrid regularizer for ExO and establish a new analysis framework for locally observable games. This development enables us to improve the existing best-of-both-worlds (BOBW) algorithms’ regret bounds, achieving nearly optimal bounds in both stochastic and adversarial environments. Specifically, we derive an O(∑a≠ak^2m^2logT/Δa) stochastic regret bound for locally observable games, which is roughly Θ(k^2logT) times smaller than existing BOBW bounds. For globally observable games, we provide a new BOBW algorithm with an O(logT) stochastic bound. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making decisions when you don’t have all the information. A technique called exploration-by-optimization (ExO) was developed to make good choices even with limited feedback. However, this approach didn’t work well in situations where things are a bit random. To fix this problem, the authors came up with a new way to use ExO that works better in both random and unfair situations. This new approach gives us more accurate predictions about how well we’ll do in different situations. |
Keywords
* Artificial intelligence * Optimization
