Summary of Online Linear Regression in Dynamic Environments Via Discounting, by Andrew Jacobsen and Ashok Cutkosky
Online Linear Regression in Dynamic Environments via Discounting
by Andrew Jacobsen, Ashok Cutkosky
First submitted to arxiv on: 29 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 develops algorithms for online linear regression, achieving optimal static and dynamic regret guarantees without prior knowledge. The Vovk-Azoury-Warmuth forecaster is analyzed to achieve dynamic regret of O(dlog(T) ∨ √(dP^γ(u)T)), where P^γ(u) measures the comparator sequence’s variability. A discount factor can be learned on-the-fly, and this result is optimal with a matching lower bound provided. The paper also extends these results to strongly-adaptive guarantees holding simultaneously over every sub-interval [a,b] ⊆ [1,T]. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions by developing new algorithms for online linear regression. Even without knowing much beforehand, the algorithms can still do a great job. The researchers found that one specific algorithm, called the Vovk-Azoury-Warmuth forecaster, does really well when it comes to making predictions. They also figured out how to make this algorithm even better by adjusting something called the discount factor on-the-fly. This is important because it means we can use these algorithms in real-life situations without needing to know too much beforehand. |
Keywords
» Artificial intelligence » Linear regression
