Establishes maximal concentration bounds for stochastic approximation under heavy-tailed Markovian noise, with tails ranging from sub-Gaussian to heavier than Weibull depending on step sizes and contractivity properties, plus a truncation argument for unbounded noise.
The Annals of Applied Probability , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Proposes Factor-Augmented SGD that runs on streaming high-dimensional data and supplies the first convergence analysis explicitly accounting for latent-factor estimation error.
citing papers explorer
-
Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise
Establishes maximal concentration bounds for stochastic approximation under heavy-tailed Markovian noise, with tails ranging from sub-Gaussian to heavier than Weibull depending on step sizes and contractivity properties, plus a truncation argument for unbounded noise.
-
Factor Augmented High-Dimensional SGD
Proposes Factor-Augmented SGD that runs on streaming high-dimensional data and supplies the first convergence analysis explicitly accounting for latent-factor estimation error.