Summary of Efficient Algorithms For Empirical Group Distributionally Robust Optimization and Beyond, by Dingzhi Yu et al.
Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyond
by Dingzhi Yu, Yunuo Cai, Wei Jiang, Lijun Zhang
First submitted to arxiv on: 6 Mar 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 investigates the empirical counterpart of Group Distributionally Robust Optimization (GDRO), which aims to minimize the maximal empirical risk across m distinct groups. The authors formulate empirical GDRO as a two-level finite-sum convex-concave minimax optimization problem and develop an algorithm called ALEG to exploit its special structure. ALEG is a double-looped stochastic primal-dual algorithm that incorporates variance reduction techniques into a modified mirror prox routine. Theoretical analysis shows that ALEG achieves ε-accuracy within a computation complexity of O( m√¯nlnm)ε, where ¯n is the average number of samples among m groups. Notably, this approach outperforms state-of-the-art methods by a factor of √m. Based on ALEG, the authors further develop a two-stage optimization algorithm called ALEM to deal with the empirical Minimax Excess Risk Optimization (MERO) problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning models more robust. Imagine you have many groups of people and each group has its own way of doing things. The goal is to make sure that a model works well across all these groups, not just one or two. To do this, the authors create an algorithm called ALEG, which is like a double-layered cake with special ingredients. This cake helps the model learn from the data and makes it more robust by reducing errors. The authors show that this approach can be faster than others and even better at dealing with tricky problems. |
Keywords
* Artificial intelligence * Machine learning * Optimization
