Summary of Online Convex Optimisation: the Optimal Switching Regret For All Segmentations Simultaneously, by Stephen Pasteris et al.
Online Convex Optimisation: The Optimal Switching Regret for all Segmentations Simultaneously
by Stephen Pasteris, Chris Hicks, Vasilios Mavroudis, Mark Herbster
First submitted to arxiv on: 31 May 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 tackles online convex optimization, focusing on non-stationary problems where traditional static regret measures are insufficient. The authors introduce switching regret, which assesses performance relative to any segmentation of the trial sequence and sums up individual segment regrets. They show that their algorithm can achieve asymptotically optimal switching regret simultaneously across all possible segmentations, while also being efficient with logarithmic space and per-trial time complexity. Additionally, the algorithm obtains novel dynamic regret bounds by adapting to variations in the comparator sequence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a tricky problem called online convex optimization. Imagine you’re trying to find the best way to optimize something over time, but it keeps changing! The authors come up with a new way to measure how well you’re doing, called switching regret. They show that their algorithm can do really well on this task while also being efficient and able to adapt to changes in the problem. |
Keywords
» Artificial intelligence » Optimization
