Summary of Dealing with Unbounded Gradients in Stochastic Saddle-point Optimization, by Gergely Neu et al.
Dealing with unbounded gradients in stochastic saddle-point optimization
by Gergely Neu, Nneka Okolo
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Machine Learning (stat.ML)
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 This paper explores the effectiveness of stochastic first-order methods in finding saddle points of convex-concave functions. The major challenge faced by these methods is the potential instability and divergence caused by growing gradients, which can become arbitrarily large during optimization. To address this issue, the authors propose a simple regularization technique that stabilizes the iterates and provides meaningful performance guarantees even when the domain and gradient noise grow linearly with the size of the iterates. The algorithm’s general results are demonstrated through an application to a specific problem in reinforcement learning, achieving performance guarantees for finding near-optimal policies in average-reward MDPs without prior knowledge of the bias span. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies ways to find saddle points using special algorithms called stochastic first-order methods. These methods can get stuck or even stop working if the gradients (which help guide the search) grow too big. To fix this, the authors came up with a simple trick to keep the gradients in check and make sure the algorithm works well even when dealing with really large or growing domains. They also tested their method on a specific problem related to making good decisions in uncertain situations, achieving results that show it can find near-optimal solutions without knowing too much about how those situations work. |
Keywords
* Artificial intelligence * Optimization * Regularization * Reinforcement learning
