Krishnakumar Balasubramanian

Publication Activity (10 Years)

Years Active: 2022-2024
Publications (10 Years): 18

Top Topics

Gaussian Random Field

Stochastic Optimization

Top Venues

J. Mach. Learn. Res.

IEEE Trans. Inf. Theory

Publications

Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu
An Analysis of Transformed Unadjusted Langevin Algorithm for Heavy-Tailed Sampling. IEEE Trans. Inf. Theory 70 (1) (2024)
Xuxing Chen, Abhishek Roy, Yifan Hu, Krishnakumar Balasubramanian
Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data. CoRR (2024)
Yanhao Jin, Krishnakumar Balasubramanian, Debashis Paul
Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation. CoRR (2024)
Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi
Stochastic Nested Compositional Bi-level Optimization for Robust Feature Learning. CoRR (2023)
Krishnakumar Balasubramanian, Larry Goldstein, Nathan Ross, Adil Salim
Gaussian random field approximation via Stein's method with applications to wide random neural networks. CoRR (2023)
Tesi Xiao, Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi
A one-sample decentralized proximal algorithm for non-convex stochastic composite optimization. UAI (2023)
Abhishek Roy, Krishnakumar Balasubramanian
Online covariance estimation for stochastic gradient descent under Markovian sampling. CoRR (2023)
Xuxing Chen, Krishnakumar Balasubramanian, Promit Ghosal, Bhavya Agrawalla
From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression. CoRR (2023)
Zhongruo Wang, Krishnakumar Balasubramanian, Shiqian Ma, Meisam Razaviyayn
Zeroth-order algorithms for nonconvex-strongly-concave minimax problems with improved complexities. J. Glob. Optim. 87 (2) (2023)
Anthony Nguyen, Krishnakumar Balasubramanian
Stochastic Zeroth-Order Functional Constrained Optimization: Oracle Complexity and Applications. INFORMS J. Optim. 5 (3) (2023)
Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian
Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions. CoRR (2023)
Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. NeurIPS (2023)
Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma
Zeroth-order Riemannian Averaging Stochastic Approximation Algorithms. CoRR (2023)
Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma
Stochastic Zeroth-Order Riemannian Derivative Estimation and Optimization. Math. Oper. Res. 48 (2) (2023)
Olympio Hacquard, Krishnakumar Balasubramanian, Gilles Blanchard, Clément Levrard, Wolfgang Polonik
Topologically penalized regression on manifolds. J. Mach. Learn. Res. 23 (2022)
Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi
Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data. NeurIPS (2022)
Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra
Stochastic Zeroth-Order Optimization under Nonstationarity and Nonconvexity. J. Mach. Learn. Res. 23 (2022)
Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi
A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization. NeurIPS (2022)