Summary of Computing Optimal Regularizers For Online Linear Optimization, by Khashayar Gatmiry et al.
Computing Optimal Regularizers for Online Linear Optimization
by Khashayar Gatmiry, Jon Schneider, Stefanie Jegelka
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Computer Science and Game Theory (cs.GT); Statistics Theory (math.ST); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Follow-the-Regularized-Leader (FTRL) algorithms are a widely used class of online linear optimization (OLO) methods that guarantee sub-linear regret. However, the choice of regularizer can significantly impact dimension-dependent factors in the regret bound. Our paper presents an algorithm that takes as input convex and symmetric action sets and loss sets for a specific OLO instance, and outputs a regularizer such that running FTRL with this regularizer guarantees regret within a universal constant factor of the best possible regret bound. Specifically, we show that for any choice of (convex, symmetric) action set and loss set, there exists an instantiation of FTRL that achieves regret within a constant factor of the best possible learning algorithm, strengthening the universality result of Srebro et al., 2011. Our algorithm can be used in various applications such as online convex optimization, where FTRL algorithms have been shown to perform well. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re playing a game where you need to make decisions based on what happens. The Follow-the-Regularized-Leader (FTRL) algorithm is a way to make these decisions that works really well. But sometimes the choices we make can affect how well the algorithm performs. Our paper shows that there’s a special kind of FTRL algorithm that can be used in any situation, and it will always perform at least as well as the best possible algorithm. This means that people using this algorithm will have more control over how well it does. This is important because it can help us make better decisions in all sorts of situations. |
Keywords
* Artificial intelligence * Optimization
