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Author search results
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- Krishnakumar Balasubramanian 0001
Mathworks Inc, Natick, MA, USA - Krishnakumar Balasubramanian 0002
University of California, Davis, Graduate Group in Applied Mathematics, CA, USA
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Publication search results
found 87 matches
- 2024
- Ye He
, Krishnakumar Balasubramanian
, Murat A. Erdogdu:
An Analysis of Transformed Unadjusted Langevin Algorithm for Heavy-Tailed Sampling. IEEE Trans. Inf. Theory 70(1): 571-593 (2024) - Yanhao Jin, Krishnakumar Balasubramanian, Debashis Paul:
Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation. CoRR abs/2403.19720 (2024) - Xuxing Chen, Abhishek Roy, Yifan Hu, Krishnakumar Balasubramanian:
Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data. CoRR abs/2405.19463 (2024) - 2023
- Anthony Nguyen
, Krishnakumar Balasubramanian
:
Stochastic Zeroth-Order Functional Constrained Optimization: Oracle Complexity and Applications. INFORMS J. Optim. 5(3): 256-272 (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): 709-740 (2023) - Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma:
Stochastic Zeroth-Order Riemannian Derivative Estimation and Optimization. Math. Oper. Res. 48(2): 1183-1211 (2023) - Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai:
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. NeurIPS 2023 - Tesi Xiao, Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi:
A one-sample decentralized proximal algorithm for non-convex stochastic composite optimization. UAI 2023: 2324-2334 - Tesi Xiao, Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi:
A One-Sample Decentralized Proximal Algorithm for Non-Convex Stochastic Composite Optimization. CoRR abs/2302.09766 (2023) - Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu:
Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling. CoRR abs/2303.00570 (2023) - Michael Diao, Krishnakumar Balasubramanian, Sinho Chewi, Adil Salim:
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space. CoRR abs/2304.05398 (2023) - Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai:
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. CoRR abs/2305.14076 (2023) - Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian:
Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions. CoRR abs/2306.12067 (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 abs/2306.16308 (2023) - Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi:
Stochastic Nested Compositional Bi-level Optimization for Robust Feature Learning. CoRR abs/2307.05384 (2023) - Abhishek Roy, Krishnakumar Balasubramanian:
Online covariance estimation for stochastic gradient descent under Markovian sampling. CoRR abs/2308.01481 (2023) - Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma:
Zeroth-order Riemannian Averaging Stochastic Approximation Algorithms. CoRR abs/2309.14506 (2023) - Xuxing Chen, Krishnakumar Balasubramanian, Promit Ghosal, Bhavya Agrawalla:
From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression. CoRR abs/2310.01687 (2023) - 2022
- Krishnakumar Balasubramanian, Saeed Ghadimi:
Zeroth-Order Nonconvex Stochastic Optimization: Handling Constraints, High Dimensionality, and Saddle Points. Found. Comput. Math. 22(1): 35-76 (2022) - Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra:
Stochastic Zeroth-Order Optimization under Nonstationarity and Nonconvexity. J. Mach. Learn. Res. 23: 64:1-64:47 (2022) - Olympio Hacquard, Krishnakumar Balasubramanian, Gilles Blanchard, Clément Levrard, Wolfgang Polonik:
Topologically penalized regression on manifolds. J. Mach. Learn. Res. 23: 161:1-161:39 (2022) - Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi:
Improved complexities for stochastic conditional gradient methods under interpolation-like conditions. Oper. Res. Lett. 50(2): 184-189 (2022) - Krishnakumar Balasubramanian
, Saeed Ghadimi
, Anthony Nguyen:
Stochastic Multilevel Composition Optimization Algorithms with Level-Independent Convergence Rates. SIAM J. Optim. 32(2): 519-544 (2022) - Kalimuthukumar Sakthivel, Rajesh Krishnasamy, Kannapiran Balasubramanian, Vijayakumar Krishnakumar, Manikandan Ganesan
:
A revolutionary Partial Resonant Inverter and doubler rectifier with MPPT based on Sliding Mode Controller for harvesting Solar photovoltaic sources. Sustain. Comput. Informatics Syst. 36: 100811 (2022) - Subhroshekhar Ghosh
, Krishnakumar Balasubramanian
, Xiaochuan Yang
:
Fractal Gaussian Networks: A Sparse Random Graph Model Based on Gaussian Multiplicative Chaos. IEEE Trans. Inf. Theory 68(5): 3234-3252 (2022) - Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu:
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance. COLT 2022: 65-102 - Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi:
Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data. NeurIPS 2022 - Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi:
A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization. NeurIPS 2022 - Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu:
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance. CoRR abs/2202.11632 (2022) - Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi:
Projection-free Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data. CoRR abs/2206.11346 (2022)
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