14 citations · 59 across the 20 of their papers we have counts for
10 papers · 1 filter
A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers
Ye He, Alireza Mousavi-Hosseini, Krishnakumar Balasubramanian +1
We study the complexity of heavy-tailed sampling and present a separation result in terms of obtaining high-accuracy versus low-accuracy guarantees i.e., samplers that require only…
Online covariance estimation for stochastic gradient descent under Markovian sampling
Abhishek Roy, Krishnakumar Balasubramanian
We investigate the online overlapping batch-means covariance estimator for Stochastic Gradient Descent (SGD) under Markovian sampling. Convergence rates of order $O\big(\sqrt{d}\,n…
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent
Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian +1
Stein Variational Gradient Descent (SVGD) is a nonparametric particle-based deterministic sampling algorithm. Despite its wide usage, understanding the theoretical properties of SV…
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space
Michael Diao, Krishnakumar Balasubramanian, Sinho Chewi +1
Variational inference (VI) seeks to approximate a target distribution by an element of a tractable family of distributions. Of key interest in statistics and machine learning i…
Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo
Krishnakumar Balasubramanian, Sinho Chewi, Murat A. Erdogdu +2
For the task of sampling from a density on , where is possibly non-convex but -gradient Lipschitz, we prove that averaged Langevin Monte Ca…
Heavy-tailed Sampling via Transformed Unadjusted Langevin Algorithm
Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu
We analyze the oracle complexity of sampling from polynomially decaying heavy-tailed target densities based on running the Unadjusted Langevin Algorithm on certain transformed vers…