Communication interception of human signal transduction pathways by Human Immunodeficiency Virus-1. - Sivaraman Balakrishnan; Öznur Tastan; Jaime G. Carbonell; Judith Klein-Seetharaman (GENSiPS, 2009)

Noise Thresholds for Spectral Clustering. - Sivaraman Balakrishnan; Min Xu 010; Akshay Krishnamurthy; Aarti Singh (NIPS, 2011)

Minimax Localization of Structural Information in Large Noisy Matrices. - Mladen Kolar; Sivaraman Balakrishnan; Alessandro Rinaldo; Aarti Singh (NIPS, 2011)

Sparse Additive Functional and Kernel CCA. - Sivaraman Balakrishnan; Kriti Puniyani; John D. Lafferty (ICML, 2012)

Efficient Active Algorithms for Hierarchical Clustering. - Akshay Krishnamurthy; Sivaraman Balakrishnan; Min Xu 010; Aarti Singh (ICML, 2012)

Optimal kernel choice for large-scale two-sample tests. - Arthur Gretton; Bharath K. Sriperumbudur; Dino Sejdinovic; Heiko Strathmann; Sivaraman Balakrishnan; Massimiliano Pontil; Kenji Fukumizu (NIPS, 2012)

Minimax rates for homology inference. - Sivaraman Balakrishnan; Alessandro Rinaldo; Don Sheehy; Aarti Singh; Larry A. Wasserman (AISTATS, 2012)

Cluster Trees on Manifolds. - Sivaraman Balakrishnan; Srivatsan Narayanan; Alessandro Rinaldo; Aarti Singh; Larry A. Wasserman (NIPS, 2013)

Tight Lower Bounds for Homology Inference. - Sivaraman Balakrishnan; Alessandro Rinaldo; Aarti Singh; Larry A. Wasserman (CoRR, 2013)

Statistical Inference For Persistent Homology - Sivaraman Balakrishnan; Brittany Terese Fasy; Fabrizio Lecci; Alessandro Rinaldo; Aarti Singh; Larry A. Wasserman (CoRR, 2013)

Statistical guarantees for the EM algorithm: From population to sample-based analysis. - Sivaraman Balakrishnan; Martin J. Wainwright; Bin Yu 001 (CoRR, 2014)

When is it Better to Compare than to Score? - Nihar B. Shah; Sivaraman Balakrishnan; Joseph K. Bradley; Abhay Parekh; Kannan Ramchandran; Martin J. Wainwright (CoRR, 2014)

Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. - Chi Jin 001; Yuchen Zhang 002; Sivaraman Balakrishnan; Martin J. Wainwright; Michael I. Jordan (NIPS, 2016)

Statistical Inference for Cluster Trees. - Jisu Kim; Yen-Chi Chen; Sivaraman Balakrishnan; Alessandro Rinaldo; Larry A. Wasserman (NIPS, 2016)

Arbitrage-Free Combinatorial Market Making via Integer Programming. - Christian Kroer; Miroslav Dudík; Sébastien Lahaie; Sivaraman Balakrishnan (EC, 2016)

Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. - Nihar B. Shah; Sivaraman Balakrishnan; Joseph K. Bradley; Abhay Parekh; Kannan Ramchandran; Martin J. Wainwright (J. Mach. Learn. Res., 2016)

Computationally Efficient Robust Sparse Estimation in High Dimensions. - Sivaraman Balakrishnan; Simon S. Du; Jerry Li 001; Aarti Singh (COLT, 2017)

Biases in Data-Driven Networking, and What to Do About Them. - Mihovil Bartulovic; Junchen Jiang; Sivaraman Balakrishnan; Vyas Sekar; Bruno Sinopoli (HotNets, 2017)

Hypothesis Testing For Densities and High-Dimensional Multinomials: Sharp Local Minimax Rates. - Sivaraman Balakrishnan; Larry A. Wasserman (CoRR, 2017)

Computationally Efficient Robust Estimation of Sparse Functionals. - Simon S. Du; Sivaraman Balakrishnan; Aarti Singh (CoRR, 2017)

Hypothesis Testing for High-Dimensional Multinomials: A Selective Review. - Sivaraman Balakrishnan; Larry A. Wasserman (CoRR, 2017)

Statistical and Computational Guarantees for the Baum-Welch Algorithm. - Fanny Yang; Sivaraman Balakrishnan; Martin J. Wainwright (J. Mach. Learn. Res., 2017)

Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues. - Nihar B. Shah; Sivaraman Balakrishnan; Adityanand Guntuboyina; Martin J. Wainwright (IEEE Trans. Inf. Theory, 2017)

Interactive Linear Regression with Pairwise Comparisons. - Yichong Xu; Sivaraman Balakrishnan; Aarti Singh; Artur Dubrawski (ACSSC, 2018)

Stochastic Zeroth-order Optimization in High Dimensions. - Yining Wang 001; Simon S. Du; Sivaraman Balakrishnan; Aarti Singh (AISTATS, 2018)

Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. - Yichong Xu; Hariank Muthakana; Sivaraman Balakrishnan; Aarti Singh; Artur Dubrawski (ICML, 2018)

How Many Samples are Needed to Estimate a Convolutional Neural Network? - Simon S. Du; Yining Wang 001; Xiyu Zhai; Sivaraman Balakrishnan; Ruslan Salakhutdinov; Aarti Singh (NeurIPS, 2018)

Local White Matter Architecture Defines Functional Brain Dynamics. - Sivaraman Balakrishnan; Yo Joong Choe; Aarti Singh; Jean M. Vettel; Timothy D. Verstynen (SMC, 2018)

Robust Estimation via Robust Gradient Estimation. - Adarsh Prasad; Arun Sai Suggala; Sivaraman Balakrishnan; Pradeep Ravikumar (CoRR, 2018)

How Many Samples are Needed to Learn a Convolutional Neural Network? - Simon S. Du; Yining Wang 001; Xiyu Zhai; Sivaraman Balakrishnan; Ruslan Salakhutdinov; Aarti Singh (CoRR, 2018)

Robust Nonparametric Regression under Huber's ε-contamination Model. - Simon S. Du; Yining Wang 001; Sivaraman Balakrishnan; Pradeep Ravikumar; Aarti Singh (CoRR, 2018)

A Unified Approach to Robust Mean Estimation. - Adarsh Prasad; Sivaraman Balakrishnan; Pradeep Ravikumar (CoRR, 2019)

Low Permutation-rank Matrices: Structural Properties and Noisy Completion. - Nihar B. Shah; Sivaraman Balakrishnan; Martin J. Wainwright (J. Mach. Learn. Res., 2019)

Rate optimal estimation and confidence intervals for high-dimensional regression with missing covariates. - Yining Wang 001; Jialei Wang; Sivaraman Balakrishnan; Aarti Singh (J. Multivar. Anal., 2019)

Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons. - Nihar B. Shah; Sivaraman Balakrishnan; Martin J. Wainwright (IEEE Trans. Inf. Theory, 2019)

Optimization of Smooth Functions With Noisy Observations: Local Minimax Rates. - Yining Wang 001; Sivaraman Balakrishnan; Aarti Singh (IEEE Trans. Inf. Theory, 2019)

A Robust Univariate Mean Estimator is All You Need. - Adarsh Prasad; Sivaraman Balakrishnan; Pradeep Ravikumar (AISTATS, 2020)

Two-Sample Testing on Pairwise Comparison Data and the Role of Modeling Assumptions. - Charvi Rastogi; Sivaraman Balakrishnan; Nihar B. Shah; Aarti Singh (ISIT, 2020)

A Unified View of Label Shift Estimation. - Saurabh Garg; Yifan Wu; Sivaraman Balakrishnan; Zachary C. Lipton (NeurIPS, 2020)

On Learning Ising Models under Huber's Contamination Model. - Adarsh Prasad; Vishwak Srinivasan; Sivaraman Balakrishnan; Pradeep Ravikumar (NeurIPS, 2020)

Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. - Yichong Xu; Sivaraman Balakrishnan; Aarti Singh; Artur Dubrawski (J. Mach. Learn. Res., 2020)

Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs. - Alden Green; Sivaraman Balakrishnan; Ryan J. Tibshirani (AISTATS, 2021)

RATT: Leveraging Unlabeled Data to Guarantee Generalization. - Saurabh Garg; Sivaraman Balakrishnan; J. Zico Kolter; Zachary C. Lipton (ICML, 2021)

On Proximal Policy Optimization's Heavy-tailed Gradients. - Saurabh Garg; Joshua Zhanson; Emilio Parisotto; Adarsh Prasad; J. Zico Kolter; Zachary C. Lipton; Sivaraman Balakrishnan; Ruslan Salakhutdinov; Pradeep Ravikumar (ICML, 2021)

Mixture Proportion Estimation and PU Learning: A Modern Approach. - Saurabh Garg; Yifan Wu; Alexander J. Smola; Sivaraman Balakrishnan; Zachary C. Lipton (NeurIPS, 2021)

Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs. - Alden Green; Sivaraman Balakrishnan; Ryan J. Tibshirani (J. Mach. Learn. Res., 2021)

Path Length Bounds for Gradient Descent and Flow. - Chirag Gupta; Sivaraman Balakrishnan; Aaditya Ramdas (J. Mach. Learn. Res., 2021)

A Permutation-Based Model for Crowd Labeling: Optimal Estimation and Robustness. - Nihar B. Shah; Sivaraman Balakrishnan; Martin J. Wainwright (IEEE Trans. Inf. Theory, 2021)

Heavy-tailed Streaming Statistical Estimation. - Che-Ping Tsai; Adarsh Prasad; Sivaraman Balakrishnan; Pradeep Ravikumar (AISTATS, 2022)

Understanding Simultaneous Train and Test Robustness. - Pranjal Awasthi; Sivaraman Balakrishnan; Aravindan Vijayaraghavan (ALT, 2022)

Leveraging unlabeled data to predict out-of-distribution performance. - Saurabh Garg; Sivaraman Balakrishnan; Zachary Chase Lipton; Behnam Neyshabur; Hanie Sedghi (ICLR, 2022)

No Rose for MLE: Inadmissibility of MLE for Evaluation Aggregation Under Levels of Expertise. - Charvi Rastogi; Ivan Stelmakh; Nihar B. Shah; Sivaraman Balakrishnan (ISIT, 2022)

Domain Adaptation under Open Set Label Shift. - Saurabh Garg; Sivaraman Balakrishnan; Zachary C. Lipton (NeurIPS, 2022)

Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions. - Charvi Rastogi; Sivaraman Balakrishnan; Nihar B. Shah; Aarti Singh (J. Mach. Learn. Res., 2022)

Domain Adaptation under Missingness Shift. - Helen Zhou; Sivaraman Balakrishnan; Zachary C. Lipton (AISTATS, 2023)

RLSbench: Domain Adaptation Under Relaxed Label Shift. - Saurabh Garg; Nick Erickson; James Sharpnack; Alex Smola; Sivaraman Balakrishnan; Zachary Chase Lipton (ICML, 2023)

Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms. - Dheeraj Baby; Saurabh Garg; Tzu-Ching Yen; Sivaraman Balakrishnan; Zachary C. Lipton; Yu-Xiang Wang 003 (NeurIPS, 2023)

Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. - Saurabh Garg; Amrith Setlur; Zachary C. Lipton; Sivaraman Balakrishnan; Virginia Smith; Aditi Raghunathan (NeurIPS, 2023)

Stability Bounds for Smooth Optimal Transport Maps and their Statistical Implications. - Sivaraman Balakrishnan; Tudor Manole (CoRR, 2025)

Statistical Inference for Optimal Transport Maps: Recent Advances and Perspectives. - Sivaraman Balakrishnan; Tudor Manole; Larry A. Wasserman (CoRR, 2025)