Summary of Adaptive Learning Rate For Follow-the-regularized-leader: Competitive Analysis and Best-of-both-worlds, by Shinji Ito et al.
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds
by Shinji Ito, Taira Tsuchiya, Junya Honda
First submitted to arxiv on: 1 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 This paper introduces a framework for adjusting the learning rate in Follow-The-Regularized-Leader (FTRL) online learning approaches. By formulating this problem as a sequential decision-making issue and using competitive analysis, the authors establish a lower bound for the competitive ratio and propose update rules that achieve an upper bound within a constant factor of this lower bound. The optimal competitive ratio is characterized by the monotonicity of penalty term components, allowing for a constant competitive ratio if these components form a monotonically non-increasing sequence. The proposed stability-penalty matching update rule facilitates constructing Best-Of-Both-Worlds (BOBW) algorithms for stochastic and adversarial environments, achieving tighter regret bounds and broadening applicability to multi-armed bandits, graph bandits, linear bandits, and contextual bandits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find the right learning rate in online learning. It’s like a game where we make decisions one step at a time. The authors show that if we choose our learning rate carefully, we can get better results. They also propose new ways to update this learning rate, which can be used in different situations like playing games or making predictions. |
Keywords
* Artificial intelligence * Online learning
