Dimensionality Reduction for Text using Domain Knowledge. - Yi Mao; Krishnakumar Balasubramanian 002; Guy Lebanon (COLING (Posters), 2010)

Asymptotic Analysis of Generative Semi-Supervised Learning. - Joshua V. Dillon; Krishnakumar Balasubramanian 002; Guy Lebanon (ICML, 2010)

Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels - Pinar Donmez; Krishnakumar Balasubramanian 002; Guy Lebanon (CoRR, 2010)

Linguistic Geometries for Unsupervised Dimensionality Reduction - Yi Mao; Krishnakumar Balasubramanian 002; Guy Lebanon (CoRR, 2010)

Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels. - Pinar Donmez; Guy Lebanon; Krishnakumar Balasubramanian 002 (J. Mach. Learn. Res., 2010)

Unsupervised Supervised Learning II: Margin-Based Classification without Labels. - Krishnakumar Balasubramanian 002; Pinar Donmez; Guy Lebanon (AISTATS, 2011)

The Landmark Selection Method for Multiple Output Prediction. - Krishnakumar Balasubramanian 002; Guy Lebanon (ICML, 2012)

Ultrahigh Dimensional Feature Screening via RKHS Embeddings. - Krishnakumar Balasubramanian 002; Bharath K. Sriperumbudur; Guy Lebanon (AISTATS, 2013)

High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning. - Krishnakumar Balasubramanian 002; Kai Yu 001; Tong Zhang 001 (UAI, 2013)

Learning matrix and functional models in high-dimensions. - Krishnakumar Balasubramanian 002 (2014)

Smooth sparse coding via marginal regression for learning sparse representations. - Krishnakumar Balasubramanian 002; Kai Yu 001; Guy Lebanon (Artif. Intell., 2016)

High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation. - Zhuoran Yang; Krishnakumar Balasubramanian 002; Han Liu 001 (ICML, 2017)

Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein's Lemma. - Zhuoran Yang; Krishnakumar Balasubramanian 002; Zhaoran Wang 001; Han Liu 001 (NIPS, 2017)

Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates. - Krishnakumar Balasubramanian 002; Saeed Ghadimi (NeurIPS, 2018)

Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT. - Andreas Anastasiou; Krishnakumar Balasubramanian 002; Murat A. Erdogdu (COLT, 2019)

Multi-Point Bandit Algorithms for Nonstationary Online Nonconvex Optimization. - Abhishek Roy 005; Krishnakumar Balasubramanian 002; Saeed Ghadimi; Prasant Mohapatra (CoRR, 2019)

Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization. - Abhishek Roy 005; Yifang Chen; Krishnakumar Balasubramanian 002; Prasant Mohapatra (CoRR, 2019)

Escaping Saddle-Point Faster under Interpolation-like Conditions. - Abhishek Roy 005; Krishnakumar Balasubramanian 002; Saeed Ghadimi; Prasant Mohapatra (NeurIPS, 2020)

On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method. - Ye He 003; Krishnakumar Balasubramanian 002; Murat A. Erdogdu (NeurIPS, 2020)

Zeroth-Order Algorithms for Nonconvex Minimax Problems with Improved Complexities. - Zhongruo Wang; Krishnakumar Balasubramanian 002; Shiqian Ma; Meisam Razaviyayn (CoRR, 2020)

Zeroth-order Optimization on Riemannian Manifolds. - Jiaxiang Li; Krishnakumar Balasubramanian 002; Shiqian Ma (CoRR, 2020)

Stochastic Multi-level Composition Optimization Algorithms with Level-Independent Convergence Rates. - Krishnakumar Balasubramanian 002; Saeed Ghadimi; Anthony Nguyen (CoRR, 2020)

Escaping Saddle-Points Faster under Interpolation-like Conditions. - Abhishek Roy 005; Krishnakumar Balasubramanian 002; Saeed Ghadimi; Prasant Mohapatra (CoRR, 2020)

On Empirical Risk Minimization with Dependent and Heavy-Tailed Data. - Abhishek Roy 005; Krishnakumar Balasubramanian 002; Murat A. Erdogdu (NeurIPS, 2021)

An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias. - Lu Yu; Krishnakumar Balasubramanian 002; Stanislav Volgushev; Murat A. Erdogdu (NeurIPS, 2021)

Statistical Inference for Polyak-Ruppert Averaged Zeroth-order Stochastic Gradient Algorithm. - Yanhao Jin; Tesi Xiao; Krishnakumar Balasubramanian 002 (CoRR, 2021)

On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests. - Krishnakumar Balasubramanian 002; Tong Li; Ming Yuan 001 (J. Mach. Learn. Res., 2021)

Nonparametric Modeling of Higher-Order Interactions via Hypergraphons. - Krishnakumar Balasubramanian 002 (J. Mach. Learn. Res., 2021)

Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance. - Nuri Mert Vural; Lu Yu; Krishnakumar Balasubramanian 002; Stanislav Volgushev; Murat A. Erdogdu (COLT, 2022)

Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data. - Abhishek Roy 005; Krishnakumar Balasubramanian 002; Saeed Ghadimi (NeurIPS, 2022)

A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization. - Tesi Xiao; Krishnakumar Balasubramanian 002; Saeed Ghadimi (NeurIPS, 2022)

Projection-free Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data. - Abhishek Roy 005; Krishnakumar Balasubramanian 002; Saeed Ghadimi (CoRR, 2022)

Decentralized Stochastic Bilevel Optimization with Improved Per-Iteration Complexity. - Xuxing Chen; Minhui Huang; Shiqian Ma; Krishnakumar Balasubramanian 002 (CoRR, 2022)

Regularized Stein Variational Gradient Flow. - Ye He 003; Krishnakumar Balasubramanian 002; Bharath K. Sriperumbudur; Jianfeng Lu (CoRR, 2022)

Zeroth-Order Nonconvex Stochastic Optimization: Handling Constraints, High Dimensionality, and Saddle Points. - Krishnakumar Balasubramanian 002; Saeed Ghadimi (Found. Comput. Math., 2022)

Stochastic Zeroth-Order Optimization under Nonstationarity and Nonconvexity. - Abhishek Roy 005; Krishnakumar Balasubramanian 002; Saeed Ghadimi; Prasant Mohapatra (J. Mach. Learn. Res., 2022)

Topologically penalized regression on manifolds. - Olympio Hacquard; Krishnakumar Balasubramanian 002; Gilles Blanchard; Clément Levrard; Wolfgang Polonik (J. Mach. Learn. Res., 2022)

Improved complexities for stochastic conditional gradient methods under interpolation-like conditions. - Tesi Xiao; Krishnakumar Balasubramanian 002; Saeed Ghadimi (Oper. Res. Lett., 2022)

Stochastic Multilevel Composition Optimization Algorithms with Level-Independent Convergence Rates. - Krishnakumar Balasubramanian 002; Saeed Ghadimi; Anthony Nguyen (SIAM J. Optim., 2022)

Fractal Gaussian Networks: A Sparse Random Graph Model Based on Gaussian Multiplicative Chaos. - Subhroshekhar Ghosh; Krishnakumar Balasubramanian 002; Xiaochuan Yang (IEEE Trans. Inf. Theory, 2022)

Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. - Tianle Liu; Promit Ghosal; Krishnakumar Balasubramanian 002; Natesh S. Pillai (NeurIPS, 2023)

A one-sample decentralized proximal algorithm for non-convex stochastic composite optimization. - Tesi Xiao; Xuxing Chen; Krishnakumar Balasubramanian 002; Saeed Ghadimi (UAI, 2023)

Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space. - Michael Diao; Krishnakumar Balasubramanian 002; Sinho Chewi; Adil Salim (CoRR, 2023)

Gaussian random field approximation via Stein's method with applications to wide random neural networks. - Krishnakumar Balasubramanian 002; Larry Goldstein; Nathan Ross; Adil Salim (CoRR, 2023)

Stochastic Nested Compositional Bi-level Optimization for Robust Feature Learning. - Xuxing Chen; Krishnakumar Balasubramanian 002; Saeed Ghadimi (CoRR, 2023)

Online covariance estimation for stochastic gradient descent under Markovian sampling. - Abhishek Roy 005; Krishnakumar Balasubramanian 002 (CoRR, 2023)

From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression. - Xuxing Chen; Krishnakumar Balasubramanian 002; Promit Ghosal; Bhavya Agrawalla (CoRR, 2023)

Stochastic Zeroth-Order Riemannian Derivative Estimation and Optimization. - Jiaxiang Li; Krishnakumar Balasubramanian 002; Shiqian Ma (Math. Oper. Res., 2023)

Stochastic Zeroth-Order Functional Constrained Optimization: Oracle Complexity and Applications. - Anthony Nguyen; Krishnakumar Balasubramanian 002 (INFORMS J. Optim., 2023)

Zeroth-order algorithms for nonconvex-strongly-concave minimax problems with improved complexities. - Zhongruo Wang; Krishnakumar Balasubramanian 002; Shiqian Ma; Meisam Razaviyayn (J. Glob. Optim., 2023)

Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data. - Xuxing Chen; Abhishek Roy 005; Yifan Hu; Krishnakumar Balasubramanian 002 (NeurIPS, 2024)

Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation. - Yanhao Jin; Krishnakumar Balasubramanian 002; Debashis Paul (CoRR, 2024)

Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent. - Krishnakumar Balasubramanian 002; Sayan Banerjee; Promit Ghosal (CoRR, 2024)

Transformers Handle Endogeneity in In-Context Linear Regression. - Haodong Liang; Krishnakumar Balasubramanian 002; Lifeng Lai (CoRR, 2024)

Provable In-context Learning for Mixture of Linear Regressions using Transformers. - Yanhao Jin; Krishnakumar Balasubramanian 002; Lifeng Lai (CoRR, 2024)

Statistical-Computational Trade-offs for Recursive Adaptive Partitioning Estimators. - Yan Shuo Tan; Jason M. Klusowski; Krishnakumar Balasubramanian 002 (CoRR, 2024)

Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions. - Xuxing Chen; Tesi Xiao; Krishnakumar Balasubramanian 002 (J. Mach. Learn. Res., 2024)

Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling. - Ye He 003; Tyler Farghly; Krishnakumar Balasubramanian 002; Murat A. Erdogdu (J. Mach. Learn. Res., 2024)

Zeroth-Order Riemannian Averaging Stochastic Approximation Algorithms. - Jiaxiang Li; Krishnakumar Balasubramanian 002; Shiqian Ma (SIAM J. Optim., 2024)

An Analysis of Transformed Unadjusted Langevin Algorithm for Heavy-Tailed Sampling. - Ye He 003; Krishnakumar Balasubramanian 002; Murat A. Erdogdu (IEEE Trans. Inf. Theory, 2024)

Riemannian Proximal Sampler for High-accuracy Sampling on Manifolds. - Yunrui Guan; Krishnakumar Balasubramanian 002; Shiqian Ma (CoRR, 2025)

Dense Associative Memory with Epanechnikov Energy. - Benjamin Hoover; Zhaoyang Shi; Krishnakumar Balasubramanian 002; Dmitry Krotov; Parikshit Ram (CoRR, 2025)

Finite-Dimensional Gaussian Approximation for Deep Neural Networks: Universality in Random Weights. - Krishnakumar Balasubramanian 002; Nathan Ross (CoRR, 2025)