54 citations · 200 across the 55 of their papers we have counts for
13 papers · 1 filter
Zeroth-order Riemannian Averaging Stochastic Approximation Algorithms
Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma
We present Zeroth-order Riemannian Averaging Stochastic Approximation (\texttt{Zo-RASA}) algorithms for stochastic optimization on Riemannian manifolds. We show that \texttt{Zo-RAS…
Stochastic Nested Compositional Bi-level Optimization for Robust Feature Learning
Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi
We develop and analyze stochastic approximation algorithms for solving nested compositional bi-level optimization problems. These problems involve a nested composition of poten…
Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions
Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian
Stochastic Bilevel optimization usually involves minimizing an upper-level (UL) function that is dependent on the arg-min of a strongly-convex lower-level (LL) function. Several al…
A One-Sample Decentralized Proximal Algorithm for Non-Convex Stochastic Composite Optimization
Tesi Xiao, Xuxing Chen, Krishnakumar Balasubramanian +1
We focus on decentralized stochastic non-convex optimization, where agents work together to optimize a composite objective function which is a sum of a smooth term and a non-sm…
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…
Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity
Xuxing Chen, Minhui Huang, Shiqian Ma +1
Bilevel optimization recently has received tremendous attention due to its great success in solving important machine learning problems like meta learning, reinforcement learning,…
Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data
Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi
We study stochastic optimization algorithms for constrained nonconvex stochastic optimization problems with Markovian data. In particular, we focus on the case when the transition…
A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization
Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi
We propose a projection-free conditional gradient-type algorithm for smooth stochastic multi-level composition optimization, where the objective function is a nested composition of…
Stochastic Multi-level Composition Optimization Algorithms with Level-Independent Convergence Rates
Krishnakumar Balasubramanian, Saeed Ghadimi, Anthony Nguyen
In this paper, we study smooth stochastic multi-level composition optimization problems, where the objective function is a nested composition of functions. We assume access to…
Improved Complexities for Stochastic Conditional Gradient Methods under Interpolation-like Conditions
Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi
We analyze stochastic conditional gradient methods for constrained optimization problems arising in over-parametrized machine learning. We show that one could leverage the interpol…
Stochastic Zeroth-order Riemannian Derivative Estimation and Optimization
Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma
We consider stochastic zeroth-order optimization over Riemannian submanifolds embedded in Euclidean space, where the task is to solve Riemannian optimization problem with only nois…
Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization
Abhishek Roy, Yifang Chen, Krishnakumar Balasubramanian +1
Saddle-point optimization problems are an important class of optimization problems with applications to game theory, multi-agent reinforcement learning and machine learning. A majo…
Zeroth-order Nonconvex Stochastic Optimization: Handling Constraints, High-Dimensionality and Saddle-Points
Krishnakumar Balasubramanian, Saeed Ghadimi
In this paper, we propose and analyze zeroth-order stochastic approximation algorithms for nonconvex and convex optimization, with a focus on addressing constrained optimization, h…