54 citations · 200 across the 55 of their papers we have counts for
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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…
Multi-Point Bandit Algorithms for Nonstationary Online Nonconvex Optimization
Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi +1
Bandit algorithms have been predominantly analyzed in the convex setting with function-value based stationary regret as the performance measure. In this paper, motivated by online…
Low-Rank Principal Eigenmatrix Analysis
Krishna Balasubramanian, Elynn Y. Chen, Jianqing Fan +1
Sparse PCA is a widely used technique for high-dimensional data analysis. In this paper, we propose a new method called low-rank principal eigenmatrix analysis. Different from spar…
Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT
Andreas Anastasiou, Krishnakumar Balasubramanian, Murat A. Erdogdu
We provide non-asymptotic convergence rates of the Polyak-Ruppert averaged stochastic gradient descent (SGD) to a normal random vector for a class of twice-differentiable test func…
Stochastic Zeroth-order Discretizations of Langevin Diffusions for Bayesian Inference
Abhishek Roy, Lingqing Shen, Krishnakumar Balasubramanian +1
Discretizations of Langevin diffusions provide a powerful method for sampling and Bayesian inference. However, such discretizations require evaluation of the gradient of the potent…