Markov Chain Gradient Descent in Hilbert Spaces
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Abstract (arXiv)
In this paper, we study a Markov chain-based stochastic gradient algorithm in general Hilbert spaces, aiming at approximating the optimal solution of a quadratic loss function. We establish probabilistic upper bounds on its convergence. We further extend these results to an online regularized learning algorithm in reproducing kernel Hilbert spaces, where the samples are drawn along a Markov chain trajectory.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 10 Oct 2024.
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References · 9
- 2018On Markov Chain Gradient Descentcited by 63
- 2020Least Squares Regression with Markovian Data: Fundamental Limits and Algorithmscited by 56
- 2023Stochastic Gradient Descent under Markovian Sampling Schemescited by 37
- 2022Adapting to Mixing Time in Stochastic Optimization with Markovian Datacited by 35
- 2020Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomnesscited by 18
- 2020Convergence Rates of Accelerated Markov Gradient Descent with Applications in Reinforcement Learningcited by 15
- 2022Stability and Generalization for Markov Chain Stochastic Gradient Methodscited by 14
- 2020Large-scale Analysis and Simulation of Traffic Flow using Markov Modelscited by 3
- 2019Markov Chain Models of Refugee Migration Datacited by 1
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