Summary of Fully Unconstrained Online Learning, by Ashok Cutkosky and Zakaria Mhammedi
Fully Unconstrained Online Learning
by Ashok Cutkosky, Zakaria Mhammedi
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
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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 algorithm achieves a regret bound of O(sqrt(T log(||w_star||G*sqrt(T)))) + ||w_star||^2 + G^2 on G-Lipschitz convex losses for any comparison point w_star without knowing either G or ||w_star||. This matches the optimal bound up to logarithmic factors, except in cases where ||w_star|| or G is large enough that even O(sqrt(T)) is roughly linear in T. The algorithm’s performance is comparable to the best possible regret bounds achievable with knowledge of G and ||w_star||. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces an online learning algorithm that can achieve sublinear regret without knowing certain values. This means it performs well over time, improving its predictions and making fewer mistakes. The algorithm’s performance is just as good as the best possible in most cases where we want to learn something new. It works by using a special type of loss function that helps it make better decisions. |
Keywords
» Artificial intelligence » Loss function » Online learning
