14 citations · 20 across the 3 of their papers we have counts for
8 papers
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance
Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian +2
We study stochastic convex optimization under infinite noise variance. Specifically, when the stochastic gradient is unbiased and has uniformly bounded -th moment, for some…
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias
Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev +1
Structured non-convex learning problems, for which critical points have favorable statistical properties, arise frequently in statistical machine learning. Algorithmic convergence…
Multiple block sizes and overlapping blocks for multivariate time series extremes
Nan Zou, Stanislav Volgushev, Axel Bücher
Block maxima methods constitute a fundamental part of the statistical toolbox in extreme value analysis. However, most of the corresponding theory is derived under the simplifying…
Testing relevant hypotheses in functional time series via self-normalization
Holger Dette, Kevin Kokot, Stanislav Volgushev
In this paper we develop methodology for testing relevant hypotheses about functional time series in a tuning-free way. Instead of testing for exact equality, for example for the e…
On Second Order Conditions in the Multivariate Block Maxima and Peak over Threshold Method
Axel Bücher, Stanislav Volgushev, Nan Zou
Second order conditions provide a natural framework for establishing asymptotic results about estimators for tail related quantities. Such conditions are typically tailored to the…
On the Unbiased Asymptotic Normality of Quantile Regression with Fixed Effects
Antonio F. Galvao, Jiaying Gu, Stanislav Volgushev
Nonlinear panel data models with fixed individual effects provide an important set of tools for describing microeconometric data. In a large class of such models (including probit,…