Summary of Central Limit Theorem For Two-timescale Stochastic Approximation with Markovian Noise: Theory and Applications, by Jie Hu et al.
Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications
by Jie Hu, Vishwaraj Doshi, Do Young Eun
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC)
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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 paper presents an in-depth asymptotic analysis of two-timescale stochastic approximation (TTSA) under controlled Markovian noise. TTSA is a general framework for iterative stochastic algorithms, encompassing well-known methods like SGD variants and reinforcement learning techniques. The authors apply the central limit theorem (CLT) to analyze the coupled dynamics of TTSA influenced by the underlying Markov chain, expanding its application horizon from vanilla SGD to distributed learning. They also leverage their CLT result to deduce the statistical properties of gradient-based temporal difference (GTD) algorithms with nonlinear function approximation using Markovian samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to analyze an important type of algorithm used in machine learning, called two-timescale stochastic approximation. This algorithm helps computers learn from experience and make good decisions. The authors wanted to understand how this algorithm works when there’s noise involved, which can happen when training models on real-world data. They did a special kind of math analysis called the central limit theorem to figure out what happens to the algorithm as it processes more data. This new understanding has practical applications in helping computers learn and make better decisions. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
