Summary of Beyond Minimax Rates in Group Distributionally Robust Optimization Via a Novel Notion Of Sparsity, by Quan Nguyen et al.
Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity
by Quan Nguyen, Nishant A. Mehta, Cristóbal Guzmán
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
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 The paper presents a breakthrough in group distributionally robust optimization (GDRO), determining its minimax sample complexity up to a log(K) factor. It introduces a novel notion of sparsity, dubbed (λ, β)-sparsity, which allows for more efficient optimization. The authors develop a novel algorithm and analysis that swap the linear dependence on K for a linear dependence on β, leveraging recent progress in sleeping bandits. This connection yields improved sample complexity bounds. Additionally, the paper presents an adaptive algorithm and dimension-free semi-adaptive sample complexity bound with computationally efficient methods. It demonstrates practicality on synthetic and real-life datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores new ways to optimize group distributionally robust optimization (GDRO). They find a way to make GDRO work better by introducing a new idea called (λ, β)-sparsity. This lets them use fewer samples to get good results. The authors also show how their ideas can be used in practice and demonstrate it on some examples. |
Keywords
* Artificial intelligence * Optimization
