Summary of The Smart Approach to Instance-optimal Online Learning, by Siddhartha Banerjee and Alankrita Bhatt and Christina Lee Yu
The SMART approach to instance-optimal online learning
by Siddhartha Banerjee, Alankrita Bhatt, Christina Lee Yu
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Information Theory (cs.IT)
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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 proposed online learning algorithm, Switching via Monotone Adapted Regret Traces (SMART), simultaneously achieves competitive performance on every input sequence compared to follow-the-leader (FTL) policies and guarantees a worst-case upper bound. SMART adapts to the data by initially playing FTL and switching at most once during the time horizon to the worst-case algorithm. This results in a regret that is instance optimal, with a multiplicative factor of e/(e-1) ≈ 1.58 compared to the smaller of the regrets obtained by FTL on the sequence and the upper bound guaranteed by the given worst-case policy. The proposed approach and results are based on an operational reduction of instance optimal online learning to competitive analysis for the ski-rental problem, with a modification that combines FTL with a “small-loss” algorithm to achieve instance optimality between the regret of FTL and the small loss regret bound. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new online learning algorithm called SMART. It’s a way to make decisions based on data that can be used in different situations. The algorithm is good because it performs well compared to other algorithms, even when the data is changing. It does this by starting with one strategy and then switching to another if needed. This approach gives a strong guarantee about how well the algorithm will perform, making it useful for real-world applications. |
Keywords
* Artificial intelligence * Online learning
