Krishnakumar Balasubramanian

94 papers A* 15A 4Journal 71Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Quantitative Characterization of Forgetting in Post-Training. CoRR Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan
2026	J	`jnl`	Dependence-Aware Label Aggregation for LLM-as-a-Judge via Ising Models. CoRR Krishnakumar Balasubramanian, Aleksandr Podkopaev, Shiva Prasad Kasiviswanathan
2026	J	`jnl`	Finite-Particle Rates for Regularized Stein Variational Gradient Descent. CoRR Ye He, Krishnakumar Balasubramanian, Sayan Banerjee, Promit Ghosal
2026	J	`jnl`	Statistical Inference for Linear Functionals of Online Least-Squares SGD When t ≳ d IEEE Trans. Inf. Theory Bhavya Agrawalla, Krishnakumar Balasubramanian, Promit Ghosal
2026	J	`jnl`	Total Variation Rates for Riemannian Flow Matching. CoRR Yunrui Guan, Krishnakumar Balasubramanian, Shiqian Ma
2025	J	`jnl`	Dense Associative Memory with Epanechnikov Energy. CoRR Benjamin Hoover, Zhaoyang Shi, Krishnakumar Balasubramanian, Dmitry Krotov, Parikshit Ram
2025	J	`jnl`	Dense associative memory on the Bures-Wasserstein space. CoRR Chandan Tankala, Krishnakumar Balasubramanian
2025	J	`jnl`	Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression. CoRR Haodong Liang, Yanhao Jin, Krishnakumar Balasubramanian, Lifeng Lai
2025	J	`jnl`	Finite-Dimensional Gaussian Approximation for Deep Neural Networks: Universality in Random Weights. CoRR Krishnakumar Balasubramanian, Nathan Ross
2025	J	`jnl`	Mirror Flow Matching with Heavy-Tailed Priors for Generative Modeling on Convex Domains. CoRR Yunrui Guan, Krishnakumar Balasubramanian, Shiqian Ma
2025	J	`jnl`	On the Structure of Stationary Solutions to McKean-Vlasov Equations with Applications to Noisy Transformers. CoRR Krishnakumar Balasubramanian, Sayan Banerjee, Philippe Rigollet
2025	J	`jnl`	Regularized Stein Variational Gradient Flow. Found. Comput. Math. Ye He, Krishnakumar Balasubramanian, Bharath K. Sriperumbudur, Jianfeng Lu
2025	J	`jnl`	Riemannian Proximal Sampler for High-accuracy Sampling on Manifolds. CoRR Yunrui Guan, Krishnakumar Balasubramanian, Shiqian Ma
2025	J	`jnl`	Statistical Inference for Linear Functionals of Online Least-squares SGD when t ≳ d CoRR Bhavya Agrawalla, Krishnakumar Balasubramanian, Promit Ghosal
2024	J	`jnl`	An Analysis of Transformed Unadjusted Langevin Algorithm for Heavy-Tailed Sampling. IEEE Trans. Inf. Theory Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu
2024	J	`jnl`	Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent. CoRR Krishnakumar Balasubramanian, Sayan Banerjee, Promit Ghosal
2024	J	`jnl`	Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling. J. Mach. Learn. Res. Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu
2024	J	`jnl`	Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation. CoRR Yanhao Jin, Krishnakumar Balasubramanian, Debashis Paul
2024	J	`jnl`	Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions. J. Mach. Learn. Res. Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian
2024	J	`jnl`	Provable In-context Learning for Mixture of Linear Regressions using Transformers. CoRR Yanhao Jin, Krishnakumar Balasubramanian, Lifeng Lai
2024	J	`jnl`	Statistical-Computational Trade-offs for Recursive Adaptive Partitioning Estimators. CoRR Yan Shuo Tan, Jason M. Klusowski, Krishnakumar Balasubramanian
2024	A*	`conf`	Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data. NeurIPS Xuxing Chen, Abhishek Roy, Yifan Hu, Krishnakumar Balasubramanian
2024	J	`jnl`	Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data. CoRR Xuxing Chen, Abhishek Roy, Yifan Hu, Krishnakumar Balasubramanian
2024	J	`jnl`	Transformers Handle Endogeneity in In-Context Linear Regression. CoRR Haodong Liang, Krishnakumar Balasubramanian, Lifeng Lai
2024	J	`jnl`	Zeroth-Order Riemannian Averaging Stochastic Approximation Algorithms. SIAM J. Optim. Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma
2023	J	`jnl`	A One-Sample Decentralized Proximal Algorithm for Non-Convex Stochastic Composite Optimization. CoRR Tesi Xiao, Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi
2023	A	`conf`	A one-sample decentralized proximal algorithm for non-convex stochastic composite optimization. UAI Tesi Xiao, Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi
2023	J	`jnl`	Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space. CoRR Michael Diao, Krishnakumar Balasubramanian, Sinho Chewi, Adil Salim
2023	J	`jnl`	From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression. CoRR Xuxing Chen, Krishnakumar Balasubramanian, Promit Ghosal, Bhavya Agrawalla
2023	J	`jnl`	Gaussian random field approximation via Stein's method with applications to wide random neural networks. CoRR Krishnakumar Balasubramanian, Larry Goldstein, Nathan Ross, Adil Salim
2023	J	`jnl`	Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling. CoRR Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu
2023	J	`jnl`	Online covariance estimation for stochastic gradient descent under Markovian sampling. CoRR Abhishek Roy, Krishnakumar Balasubramanian
2023	J	`jnl`	Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions. CoRR Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian
2023	J	`jnl`	Stochastic Nested Compositional Bi-level Optimization for Robust Feature Learning. CoRR Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi
2023	J	`jnl`	Stochastic Zeroth-Order Functional Constrained Optimization: Oracle Complexity and Applications. INFORMS J. Optim. Anthony Nguyen, Krishnakumar Balasubramanian
2023	J	`jnl`	Stochastic Zeroth-Order Riemannian Derivative Estimation and Optimization. Math. Oper. Res. Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma
2023	A*	`conf`	Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. NeurIPS Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai
2023	J	`jnl`	Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. CoRR Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai
2023	J	`jnl`	Zeroth-order Riemannian Averaging Stochastic Approximation Algorithms. CoRR Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma
2023	J	`jnl`	Zeroth-order algorithms for nonconvex-strongly-concave minimax problems with improved complexities. J. Glob. Optim. Zhongruo Wang, Krishnakumar Balasubramanian, Shiqian Ma, Meisam Razaviyayn
2022	A*	`conf`	A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization. NeurIPS Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi
2022	A*	`conf`	Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data. NeurIPS Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi
2022	J	`jnl`	Decentralized Stochastic Bilevel Optimization with Improved Per-Iteration Complexity. CoRR Xuxing Chen, Minhui Huang, Shiqian Ma, Krishnakumar Balasubramanian
2022	J	`jnl`	Fractal Gaussian Networks: A Sparse Random Graph Model Based on Gaussian Multiplicative Chaos. IEEE Trans. Inf. Theory Subhroshekhar Ghosh, Krishnakumar Balasubramanian, Xiaochuan Yang
2022	J	`jnl`	Improved complexities for stochastic conditional gradient methods under interpolation-like conditions. Oper. Res. Lett. Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi
2022	A*	`conf`	Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance. COLT Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu
2022	J	`jnl`	Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance. CoRR Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu
2022	J	`jnl`	Projection-free Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data. CoRR Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi
2022	J	`jnl`	Regularized Stein Variational Gradient Flow. CoRR Ye He, Krishnakumar Balasubramanian, Bharath K. Sriperumbudur, Jianfeng Lu
2022	J	`jnl`	Stochastic Multilevel Composition Optimization Algorithms with Level-Independent Convergence Rates. SIAM J. Optim. Krishnakumar Balasubramanian, Saeed Ghadimi, Anthony Nguyen
2022	J	`jnl`	Stochastic Zeroth-Order Optimization under Nonstationarity and Nonconvexity. J. Mach. Learn. Res. Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra
2022	J	`jnl`	Topologically penalized regression on manifolds. J. Mach. Learn. Res. Olympio Hacquard, Krishnakumar Balasubramanian, Gilles Blanchard, Clément Levrard, Wolfgang Polonik
2022	J	`jnl`	Zeroth-Order Nonconvex Stochastic Optimization: Handling Constraints, High Dimensionality, and Saddle Points. Found. Comput. Math. Krishnakumar Balasubramanian, Saeed Ghadimi
2021	A*	`conf`	An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias. NeurIPS Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu
2021	J	`jnl`	Nonparametric Modeling of Higher-Order Interactions via Hypergraphons. CoRR Krishnakumar Balasubramanian
2021	J	`jnl`	Nonparametric Modeling of Higher-Order Interactions via Hypergraphons. J. Mach. Learn. Res. Krishnakumar Balasubramanian
2021	A*	`conf`	On Empirical Risk Minimization with Dependent and Heavy-Tailed Data. NeurIPS Abhishek Roy, Krishnakumar Balasubramanian, Murat A. Erdogdu
2021	J	`jnl`	On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests. J. Mach. Learn. Res. Krishnakumar Balasubramanian, Tong Li, Ming Yuan
2021	J	`jnl`	Statistical Inference for Polyak-Ruppert Averaged Zeroth-order Stochastic Gradient Algorithm. CoRR Yanhao Jin, Tesi Xiao, Krishnakumar Balasubramanian
2021	J	`jnl`	Topologically penalized regression on manifolds. CoRR Olympio Hacquard, Krishnakumar Balasubramanian, Gilles Blanchard, Wolfgang Polonik, Clément Levrard
2020	J	`jnl`	An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias. CoRR Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu
2020	A*	`conf`	Escaping Saddle-Point Faster under Interpolation-like Conditions. NeurIPS Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra
2020	J	`jnl`	Escaping Saddle-Points Faster under Interpolation-like Conditions. CoRR Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra
2020	A*	`conf`	Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos. ICML Subhroshekhar Ghosh, Krishnakumar Balasubramanian, Xiaochuan Yang
2020	J	`jnl`	Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos. CoRR Subhroshekhar Ghosh, Krishnakumar Balasubramanian, Xiaochuan Yang
2020	J	`jnl`	Improved Complexities for Stochastic Conditional Gradient Methods under Interpolation-like Conditions. CoRR Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi
2020	A*	`conf`	On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method. NeurIPS Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu
2020	J	`jnl`	On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method. CoRR Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu
2020	J	`jnl`	Stochastic Multi-level Composition Optimization Algorithms with Level-Independent Convergence Rates. CoRR Krishnakumar Balasubramanian, Saeed Ghadimi, Anthony Nguyen
2020	J	`jnl`	Zeroth-Order Algorithms for Nonconvex Minimax Problems with Improved Complexities. CoRR Zhongruo Wang, Krishnakumar Balasubramanian, Shiqian Ma, Meisam Razaviyayn
2020	J	`jnl`	Zeroth-order Optimization on Riemannian Manifolds. CoRR Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma
2019	J	`jnl`	Multi-Point Bandit Algorithms for Nonstationary Online Nonconvex Optimization. CoRR Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra
2019	A*	`conf`	Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT. COLT Andreas Anastasiou, Krishnakumar Balasubramanian, Murat A. Erdogdu
2019	J	`jnl`	Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization. CoRR Abhishek Roy, Yifang Chen, Krishnakumar Balasubramanian, Prasant Mohapatra
2018	A*	`conf`	Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates. NeurIPS Krishnakumar Balasubramanian, Saeed Ghadimi
2018	J	`jnl`	Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates. CoRR Krishnakumar Balasubramanian, Saeed Ghadimi
2017	—	`conf`	Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein's Lemma. NIPS Zhuoran Yang, Krishnakumar Balasubramanian, Zhaoran Wang, Han Liu
2017	A*	`conf`	High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation. ICML Zhuoran Yang, Krishnakumar Balasubramanian, Han Liu
2016	J	`jnl`	Smooth sparse coding via marginal regression for learning sparse representations. Artif. Intell. Krishnakumar Balasubramanian, Kai Yu, Guy Lebanon
2014	—	`—`	Learning matrix and functional models in high-dimensions. Krishnakumar Balasubramanian
2013	A	`conf`	High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning. UAI Krishnakumar Balasubramanian, Kai Yu, Tong Zhang
2013	J	`jnl`	High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning. CoRR Krishnakumar Balasubramanian, Kai Yu, Tong Zhang
2013	—	`conf`	Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations. ICML (3) Krishnakumar Balasubramanian, Kai Yu, Guy Lebanon
2013	A	`conf`	Ultrahigh Dimensional Feature Screening via RKHS Embeddings. AISTATS Krishnakumar Balasubramanian, Bharath K. Sriperumbudur, Guy Lebanon
2012	J	`jnl`	Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations CoRR Krishnakumar Balasubramanian, Kai Yu, Guy Lebanon
2012	A*	`conf`	The Landmark Selection Method for Multiple Output Prediction. ICML Krishnakumar Balasubramanian, Guy Lebanon
2011	J	`jnl`	Unsupervised Supervised Learning II: Margin-Based Classification Without Labels. J. Mach. Learn. Res. Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon
2011	A	`conf`	Unsupervised Supervised Learning II: Margin-Based Classification without Labels. AISTATS Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon
2010	J	`jnl`	Asymptotic Analysis of Generative Semi-Supervised Learning CoRR Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon
2010	A*	`conf`	Asymptotic Analysis of Generative Semi-Supervised Learning. ICML Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon
2010	—	`conf`	Dimensionality Reduction for Text using Domain Knowledge. COLING (Posters) Yi Mao, Krishnakumar Balasubramanian, Guy Lebanon
2010	J	`jnl`	Linguistic Geometries for Unsupervised Dimensionality Reduction CoRR Yi Mao, Krishnakumar Balasubramanian, Guy Lebanon
2010	J	`jnl`	Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels. J. Mach. Learn. Res. Pinar Donmez, Guy Lebanon, Krishnakumar Balasubramanian
2010	J	`jnl`	Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels CoRR Pinar Donmez, Krishnakumar Balasubramanian, Guy Lebanon