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Krishnakumar Balasubramanian 0002
Person information
- affiliation: University of California, Davis, Graduate Group in Applied Mathematics, CA, USA
- affiliation (PhD 2014): Georgia Institute of Technology, Atlanta, GA, USA
Other persons with the same name
- Krishnakumar Balasubramanian
- Krishnakumar Balasubramanian 0001 — Mathworks Inc, Natick, MA, USA (and 1 more)
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2020 – today
- 2024
- [j18]Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu:
Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling. J. Mach. Learn. Res. 25: 43:1-43:44 (2024) - [j17]Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian:
Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions. J. Mach. Learn. Res. 25: 151:1-151:51 (2024) - [j16]Jiaxiang Li, Krishnakumar Balasubramanian
, Shiqian Ma
Zeroth-Order Riemannian Averaging Stochastic Approximation Algorithms. SIAM J. Optim. 34(4): 3314-3341 (2024) - [j15]Ye He
An Analysis of Transformed Unadjusted Langevin Algorithm for Heavy-Tailed Sampling. IEEE Trans. Inf. Theory 70(1): 571-593 (2024) - [i37]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) - [i36]Xuxing Chen, Abhishek Roy, Yifan Hu, Krishnakumar Balasubramanian:
Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data. CoRR abs/2405.19463 (2024) - [i35]Krishnakumar Balasubramanian, Sayan Banerjee, Promit Ghosal:
Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent. CoRR abs/2409.08469 (2024) - [i34]Yanhao Jin, Krishnakumar Balasubramanian, Lifeng Lai:
Provable In-context Learning for Mixture of Linear Regressions using Transformers. CoRR abs/2410.14183 (2024) - 2023
- [j14]Anthony Nguyen
Stochastic Zeroth-Order Functional Constrained Optimization: Oracle Complexity and Applications. INFORMS J. Optim. 5(3): 256-272 (2023) - [j13]Zhongruo Wang
Zeroth-order algorithms for nonconvex-strongly-concave minimax problems with improved complexities. J. Glob. Optim. 87(2): 709-740 (2023) - [j12]Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma:
Stochastic Zeroth-Order Riemannian Derivative Estimation and Optimization. Math. Oper. Res. 48(2): 1183-1211 (2023) - [c21]Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai:
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. NeurIPS 2023 - [c20]Tesi Xiao, Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi:
A one-sample decentralized proximal algorithm for non-convex stochastic composite optimization. UAI 2023: 2324-2334 - [i33]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) - [i32]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) - [i31]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) - [i30]Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai:
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. CoRR abs/2305.14076 (2023) - [i29]Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian:
Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions. CoRR abs/2306.12067 (2023) - [i28]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) - [i27]Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi:
Stochastic Nested Compositional Bi-level Optimization for Robust Feature Learning. CoRR abs/2307.05384 (2023) - [i26]Abhishek Roy, Krishnakumar Balasubramanian:
Online covariance estimation for stochastic gradient descent under Markovian sampling. CoRR abs/2308.01481 (2023) - [i25]Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma:
Zeroth-order Riemannian Averaging Stochastic Approximation Algorithms. CoRR abs/2309.14506 (2023) - [i24]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
- [j11]Krishnakumar Balasubramanian, Saeed Ghadimi:
Zeroth-Order Nonconvex Stochastic Optimization: Handling Constraints, High Dimensionality, and Saddle Points. Found. Comput. Math. 22(1): 35-76 (2022) - [j10]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) - [j9]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) - [j8]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) - [j7]Krishnakumar Balasubramanian
Stochastic Multilevel Composition Optimization Algorithms with Level-Independent Convergence Rates. SIAM J. Optim. 32(2): 519-544 (2022) - [j6]Subhroshekhar Ghosh
Fractal Gaussian Networks: A Sparse Random Graph Model Based on Gaussian Multiplicative Chaos. IEEE Trans. Inf. Theory 68(5): 3234-3252 (2022) - [c19]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 - [c18]Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi:
Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data. NeurIPS 2022 - [c17]Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi:
A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization. NeurIPS 2022 - [i23]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) - [i22]Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi:
Projection-free Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data. CoRR abs/2206.11346 (2022) - [i21]Xuxing Chen, Minhui Huang, Shiqian Ma, Krishnakumar Balasubramanian:
Decentralized Stochastic Bilevel Optimization with Improved Per-Iteration Complexity. CoRR abs/2210.12839 (2022) - [i20]Ye He, Krishnakumar Balasubramanian, Bharath K. Sriperumbudur, Jianfeng Lu:
Regularized Stein Variational Gradient Flow. CoRR abs/2211.07861 (2022) - 2021
- [j5]Krishnakumar Balasubramanian, Tong Li, Ming Yuan:
On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests. J. Mach. Learn. Res. 22: 1:1-1:45 (2021) - [j4]Krishnakumar Balasubramanian:
Nonparametric Modeling of Higher-Order Interactions via Hypergraphons. J. Mach. Learn. Res. 22: 146:1-146:35 (2021) - [c16]Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu:
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias. NeurIPS 2021: 4234-4248 - [c15]Abhishek Roy, Krishnakumar Balasubramanian, Murat A. Erdogdu:
On Empirical Risk Minimization with Dependent and Heavy-Tailed Data. NeurIPS 2021: 8913-8926 - [i19]Yanhao Jin, Tesi Xiao, Krishnakumar Balasubramanian:
Statistical Inference for Polyak-Ruppert Averaged Zeroth-order Stochastic Gradient Algorithm. CoRR abs/2102.05198 (2021) - [i18]Krishnakumar Balasubramanian:
Nonparametric Modeling of Higher-Order Interactions via Hypergraphons. CoRR abs/2105.08678 (2021) - [i17]Olympio Hacquard, Krishnakumar Balasubramanian, Gilles Blanchard, Wolfgang Polonik, Clément Levrard:
Topologically penalized regression on manifolds. CoRR abs/2110.13749 (2021) - 2020
- [c14]Subhroshekhar Ghosh, Krishnakumar Balasubramanian, Xiaochuan Yang:
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos. ICML 2020: 3545-3555 - [c13]Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra:
Escaping Saddle-Point Faster under Interpolation-like Conditions. NeurIPS 2020 - [c12]Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu:
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method. NeurIPS 2020 - [i16]Zhongruo Wang
Zeroth-Order Algorithms for Nonconvex Minimax Problems with Improved Complexities. CoRR abs/2001.07819 (2020) - [i15]Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma:
Zeroth-order Optimization on Riemannian Manifolds. CoRR abs/2003.11238 (2020) - [i14]Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu:
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias. CoRR abs/2006.07904 (2020) - [i13]Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi:
Improved Complexities for Stochastic Conditional Gradient Methods under Interpolation-like Conditions. CoRR abs/2006.08167 (2020) - [i12]Subhroshekhar Ghosh, Krishnakumar Balasubramanian, Xiaochuan Yang:
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos. CoRR abs/2008.03038 (2020) - [i11]Krishnakumar Balasubramanian, Saeed Ghadimi, Anthony Nguyen:
Stochastic Multi-level Composition Optimization Algorithms with Level-Independent Convergence Rates. CoRR abs/2008.10526 (2020) - [i10]Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra:
Escaping Saddle-Points Faster under Interpolation-like Conditions. CoRR abs/2009.13016 (2020) - [i9]Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu:
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method. CoRR abs/2011.03176 (2020)
2010 – 2019
- 2019
- [c11]Andreas Anastasiou, Krishnakumar Balasubramanian, Murat A. Erdogdu:
Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT. COLT 2019: 115-137 - [i8]Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra:
Multi-Point Bandit Algorithms for Nonstationary Online Nonconvex Optimization. CoRR abs/1907.13616 (2019) - [i7]Abhishek Roy, Yifang Chen, Krishnakumar Balasubramanian, Prasant Mohapatra:
Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization. CoRR abs/1912.01698 (2019) - 2018
- [c10]Krishnakumar Balasubramanian, Saeed Ghadimi:
Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates. NeurIPS 2018: 3459-3468 - [i6]Krishnakumar Balasubramanian, Saeed Ghadimi:
Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates. CoRR abs/1809.06474 (2018) - 2017
- [c9]Zhuoran Yang, Krishnakumar Balasubramanian, Han Liu:
High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation. ICML 2017: 3851-3860 - [c8]Zhuoran Yang, Krishnakumar Balasubramanian, Zhaoran Wang, Han Liu:
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein's Lemma. NIPS 2017: 6097-6106 - 2016
- [j3]Krishnakumar Balasubramanian, Kai Yu, Guy Lebanon:
Smooth sparse coding via marginal regression for learning sparse representations. Artif. Intell. 238: 83-95 (2016) - 2014
- [b1]Krishnakumar Balasubramanian:
Learning matrix and functional models in high-dimensions. Georgia Institute of Technology, Atlanta, GA, USA, 2014 - 2013
- [c7]Krishnakumar Balasubramanian, Bharath K. Sriperumbudur, Guy Lebanon:
Ultrahigh Dimensional Feature Screening via RKHS Embeddings. AISTATS 2013: 126-134 - [c6]Krishnakumar Balasubramanian, Kai Yu, Guy Lebanon:
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations. ICML (3) 2013: 289-297 - [c5]Krishnakumar Balasubramanian, Kai Yu, Tong Zhang:
High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning. UAI 2013 - [i5]Krishnakumar Balasubramanian, Kai Yu, Tong Zhang:
High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning. CoRR abs/1309.6814 (2013) - 2012
- [c4]Krishnakumar Balasubramanian, Guy Lebanon:
The Landmark Selection Method for Multiple Output Prediction. ICML 2012 - [i4]Krishnakumar Balasubramanian, Kai Yu, Guy Lebanon:
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations. CoRR abs/1210.1121 (2012) - 2011
- [j2]Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon:
Unsupervised Supervised Learning II: Margin-Based Classification Without Labels. J. Mach. Learn. Res. 12: 3119-3145 (2011) - [c3]Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon:
Unsupervised Supervised Learning II: Margin-Based Classification without Labels. AISTATS 2011: 137-145 - 2010
- [j1]Pinar Donmez, Guy Lebanon, Krishnakumar Balasubramanian:
Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels. J. Mach. Learn. Res. 11: 1323-1351 (2010) - [c2]Yi Mao, Krishnakumar Balasubramanian, Guy Lebanon:
Dimensionality Reduction for Text using Domain Knowledge. COLING (Posters) 2010: 801-809 - [c1]Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon:
Asymptotic Analysis of Generative Semi-Supervised Learning. ICML 2010: 295-302 - [i3]Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon:
Asymptotic Analysis of Generative Semi-Supervised Learning. CoRR abs/1003.0024 (2010) - [i2]Pinar Donmez, Krishnakumar Balasubramanian, Guy Lebanon:
Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels. CoRR abs/1003.0470 (2010) - [i1]Yi Mao, Krishnakumar Balasubramanian, Guy Lebanon:
Linguistic Geometries for Unsupervised Dimensionality Reduction. CoRR abs/1003.0628 (2010)
Coauthor Index
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