14 citations · 57 across the 13 of their papers we have counts for
19 papers
Stochastic Zeroth-order Functional Constrained Optimization: Oracle Complexity and Applications
Anthony Nguyen, Krishnakumar Balasubramanian
Functionally constrained stochastic optimization problems, where neither the objective function nor the constraint functions are analytically available, arise frequently in machine…
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…
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…
On Empirical Risk Minimization with Dependent and Heavy-Tailed Data
Abhishek Roy, Krishnakumar Balasubramanian, Murat A. Erdogdu
In this work, we establish risk bounds for the Empirical Risk Minimization (ERM) with both dependent and heavy-tailed data-generating processes. We do so by extending the seminal w…
Nonparametric Modeling of Higher-Order Interactions via Hypergraphons
Krishnakumar Balasubramanian
We study statistical and algorithmic aspects of using hypergraphons, that are limits of large hypergraphs, for modeling higher-order interactions. Although hypergraphons are extrem…