mlsanityData to 7 Oct 2026

Asymptotic Time-Uniform Inference for Parameters in Averaged Stochastic Approximation

arXiv 2410.15057 ↗First posted 19 Oct 2024stat.ML cs.LG stat.MEcited by 3

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Abstract (arXiv)

We study time-uniform statistical inference for parameters in stochastic approximation (SA), which encompasses a bunch of applications in optimization and machine learning. To that end, we analyze the almost-sure convergence rates of the averaged iterates to a scaled sum of Gaussians in both linear and nonlinear SA problems. We then construct three types of asymptotic confidence sequences that are valid uniformly across all times with coverage guarantees, in an asymptotic sense that the starting time is sufficiently large. These coverage guarantees remain valid if the unknown covariance matrix is replaced by its plug-in estimator, and we conduct experiments to validate our methodology.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 22 of its 56 references that are in the map. First posted on arXiv 19 Oct 2024.

Builds on

22 of 56 references.

References · 22

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10 more
  1. 2019Sequential estimation of quantiles with applications to A/B-testing and best-arm identificationcited by 27
  2. 2023Online Statistical Inference for Nonlinear Stochastic Approximation with Markovian Datacited by 18
  3. 2020Statistical Inference for Online Decision-Making: In a Contextual Bandit Settingcited by 17
  4. 2020Statistical Inference for Online Decision Making via Stochastic Gradient Descentcited by 17
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  7. 2019On Constructing Confidence Region for Model Parameters in Stochastic Gradient Descent via Batch Meanscited by 13
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Federated Learning of Quantile Inference under Local Differential Privacycited by 2
  2. 2026Sharp asymptotic theory for Q-learning with LDTZ learning rate and its generalizationcited by 0
  3. 2026Statistical Inference on Gradient Flowscited by 0