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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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4 results for “ROOT-SGD”

Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsizestat.ML

Enhanced ROOT-SGD optimizes stochastic optimization with diminishing stepsizes.

problem Improving statistical efficiency in stochastic optimization.

method Integrates a diminishing stepsize strategy into ROOT-SGD.

result Achieves optimal convergence rates with improved stability and precision.

ROOT-SGD stochastic optimization statistical efficiency diminishing stepsizes convergence rates

2024-07-15abs ↗pdf ↗

ROOT-SGD: Sharp Nonasymptotics and Near-Optimal Asymptotics in a Single Algorithmmath.OC

ROOT-SGD solves convex optimization problems with optimal nonasymptotic and near-optimal asymptotic performance.

problem Solving strongly convex and smooth unconstrained optimization problems using stochastic first-order algorithms.

method ROOT-SGD: Recursive One-Over-T SGD, averaging past stochastic gradients.

result Achieves state-of-the-art performance in both nonasymptotic and asymptotic senses.

ROOT-SGD convex optimization stochastic algorithms sharp nonasymptotics near-optimal asymptotics

2020-08-28abs ↗pdf ↗

Covariance Estimators for the ROOT-SGD Algorithm in Online Learningstat.ML

Two new covariance estimators for ROOT-SGD improve statistical inference.

problem Uncertainty measurement for ROOT-SGD's normal distribution estimator.

method Developed two covariance estimators: plug-in and Hessian-free.

result Hessian-free estimator is asymptotically consistent and Hessian-free.

ROOT-SGD covariance estimation online learning stochastic gradient statistical inference

2022-12-02abs ↗pdf ↗

Statistical Inference for Online Algorithmsstat.ML

A method for efficient statistical inference from online algorithms.

problem Computational constraints in online algorithms make traditional variance estimation difficult.

method HulC method that wraps around online algorithms to produce valid confidence regions.

result The HulC method produces asymptotically valid confidence regions for online algorithms.

statistical inference online algorithms confidence intervals HulC method Stochastic Gradient Descent

2025-05-22abs ↗pdf ↗