Sivaraman Balakrishnan

Publication Activity (10 Years)

Years Active: 2009-2023
Publications (10 Years): 77

Top Topics

Robust Estimation

Pairwise Comparison

High Dimensions

Top Venues

J. Mach. Learn. Res.

Publications

Helen Zhou, Sivaraman Balakrishnan, Zachary C. Lipton
Domain Adaptation under Missingness Shift. AISTATS (2023)
Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton
RLSbench: Domain Adaptation Under Relaxed Label Shift. CoRR (2023)
Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary C. Lipton, Yu-Xiang Wang
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms. NeurIPS (2023)
Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton
RLSbench: Domain Adaptation Under Relaxed Label Shift. ICML (2023)
Saurabh Garg, Amrith Setlur, Zachary C. Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. NeurIPS (2023)
Saurabh Garg, Amrith Setlur, Zachary Chase Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. CoRR (2023)
Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary Chase Lipton, Yu-Xiang Wang
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms. CoRR (2023)
Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, Hanie Sedghi
Leveraging unlabeled data to predict out-of-distribution performance. ICLR (2022)
Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton
Domain Adaptation under Open Set Label Shift. NeurIPS (2022)
Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions. J. Mach. Learn. Res. 23 (2022)
Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
Heavy-tailed Streaming Statistical Estimation. AISTATS (2022)
Charvi Rastogi, Ivan Stelmakh, Nihar B. Shah, Sivaraman Balakrishnan
No Rose for MLE: Inadmissibility of MLE for Evaluation Aggregation Under Levels of Expertise. ISIT (2022)
Pranjal Awasthi, Sivaraman Balakrishnan, Aravindan Vijayaraghavan
Understanding Simultaneous Train and Test Robustness. ALT (2022)
Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton
Domain Adaptation under Open Set Label Shift. CoRR (2022)
Helen Zhou, Sivaraman Balakrishnan, Zachary C. Lipton
Domain Adaptation under Missingness Shift. CoRR (2022)
Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton, Behnam Neyshabur, Hanie Sedghi
Leveraging Unlabeled Data to Predict Out-of-Distribution Performance. CoRR (2022)
Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas
Path Length Bounds for Gradient Descent and Flow. J. Mach. Learn. Res. 22 (2021)
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
A Permutation-Based Model for Crowd Labeling: Optimal Estimation and Robustness. IEEE Trans. Inf. Theory 67 (6) (2021)
Saurabh Garg, Yifan Wu, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton
Mixture Proportion Estimation and PU Learning: A Modern Approach. CoRR (2021)
Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Sivaraman Balakrishnan, Zachary C. Lipton, Ruslan Salakhutdinov, Pradeep Ravikumar
On Proximal Policy Optimization's Heavy-tailed Gradients. CoRR (2021)
Saurabh Garg, Yifan Wu, Alexander J. Smola, Sivaraman Balakrishnan, Zachary C. Lipton
Mixture Proportion Estimation and PU Learning: A Modern Approach. NeurIPS (2021)
Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs. J. Mach. Learn. Res. 22 (2021)
Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary C. Lipton
RATT: Leveraging Unlabeled Data to Guarantee Generalization. CoRR (2021)
Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs. AISTATS (2021)
Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Zachary Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Ravikumar
On Proximal Policy Optimization's Heavy-tailed Gradients. ICML (2021)
Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
Heavy-tailed Streaming Statistical Estimation. CoRR (2021)
Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary Lipton
RATT: Leveraging Unlabeled Data to Guarantee Generalization. ICML (2021)
Adarsh Prasad, Vishwak Srinivasan, Sivaraman Balakrishnan, Pradeep Ravikumar
On Learning Ising Models under Huber's Contamination Model. NeurIPS (2020)
Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski
Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. J. Mach. Learn. Res. 21 (2020)
Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions. CoRR (2020)
Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton
A Unified View of Label Shift Estimation. NeurIPS (2020)
Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton
A Unified View of Label Shift Estimation. CoRR (2020)
Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
A Robust Univariate Mean Estimator is All You Need. AISTATS (2020)
Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh
Two-Sample Testing on Pairwise Comparison Data and the Role of Modeling Assumptions. ISIT (2020)
Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
A Unified Approach to Robust Mean Estimation. CoRR (2019)
Yining Wang, Sivaraman Balakrishnan, Aarti Singh
Optimization of Smooth Functions With Noisy Observations: Local Minimax Rates. IEEE Trans. Inf. Theory 65 (11) (2019)
Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas
Path Length Bounds for Gradient Descent and Flow. CoRR (2019)
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons. IEEE Trans. Inf. Theory 65 (8) (2019)
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
Low Permutation-rank Matrices: Structural Properties and Noisy Completion. J. Mach. Learn. Res. 20 (2019)
Yining Wang, Jialei Wang, Sivaraman Balakrishnan, Aarti Singh
Rate optimal estimation and confidence intervals for high-dimensional regression with missing covariates. J. Multivar. Anal. 174 (2019)
Simon S. Du, Yining Wang, Sivaraman Balakrishnan, Pradeep Ravikumar, Aarti Singh
Robust Nonparametric Regression under Huber's ε-contamination Model. CoRR (2018)
Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Aarti Singh
How Many Samples are Needed to Estimate a Convolutional Neural Network? NeurIPS (2018)
Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. ICML (2018)
Yining Wang, Sivaraman Balakrishnan, Aarti Singh
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates. CoRR (2018)
Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. CoRR (2018)
Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Aarti Singh
How Many Samples are Needed to Learn a Convolutional Neural Network? CoRR (2018)
Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan, Pradeep Ravikumar
Robust Estimation via Robust Gradient Estimation. CoRR (2018)
Yining Wang, Simon S. Du, Sivaraman Balakrishnan, Aarti Singh
Stochastic Zeroth-order Optimization in High Dimensions. AISTATS (2018)
Sivaraman Balakrishnan, Yo Joong Choe, Aarti Singh, Jean M. Vettel, Timothy D. Verstynen
Local White Matter Architecture Defines Functional Brain Dynamics. SMC (2018)
Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski
Interactive Linear Regression with Pairwise Comparisons. ACSSC (2018)
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
Low Permutation-Rank Matrices: Structural Properties and Noisy Completion. ISIT (2018)
Yining Wang, Sivaraman Balakrishnan, Aarti Singh
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates. NeurIPS (2018)
Mihovil Bartulovic, Junchen Jiang, Sivaraman Balakrishnan, Vyas Sekar, Bruno Sinopoli
Biases in Data-Driven Networking, and What to Do About Them. HotNets (2017)
Yining Wang, Simon S. Du, Sivaraman Balakrishnan, Aarti Singh
Stochastic Zeroth-order Optimization in High Dimensions. CoRR (2017)
Simon S. Du, Sivaraman Balakrishnan, Aarti Singh
Computationally Efficient Robust Estimation of Sparse Functionals. CoRR (2017)
Sivaraman Balakrishnan, Larry A. Wasserman
Hypothesis Testing for High-Dimensional Multinomials: A Selective Review. CoRR (2017)
Yining Wang, Jialei Wang, Sivaraman Balakrishnan, Aarti Singh
Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates. CoRR (2017)
Nihar B. Shah, Sivaraman Balakrishnan, Adityanand Guntuboyina, Martin J. Wainwright
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues. IEEE Trans. Inf. Theory 63 (2) (2017)
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
Low Permutation-rank Matrices: Structural Properties and Noisy Completion. CoRR (2017)
Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright
Statistical and Computational Guarantees for the Baum-Welch Algorithm. J. Mach. Learn. Res. 18 (2017)
Sivaraman Balakrishnan, Simon S. Du, Jerry Li, Aarti Singh
Computationally Efficient Robust Sparse Estimation in High Dimensions. COLT (2017)
Sivaraman Balakrishnan, Larry A. Wasserman
Hypothesis Testing For Densities and High-Dimensional Multinomials: Sharp Local Minimax Rates. CoRR (2017)
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons. CoRR (2016)
Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. CoRR (2016)
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness. CoRR (2016)
Nihar B. Shah, Sivaraman Balakrishnan, Aditya Guntuboyina, Martin J. Wainwright
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues. ICML (2016)
Nihar B. Shah, Sivaraman Balakrishnan, Joseph K. Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. J. Mach. Learn. Res. 17 (2016)
Christian Kroer, Miroslav Dudík, Sébastien Lahaie, Sivaraman Balakrishnan
Arbitrage-Free Combinatorial Market Making via Integer Programming. EC (2016)
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
Feeling the bern: Adaptive estimators for Bernoulli probabilities of pairwise comparisons. ISIT (2016)
Jisu Kim, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry A. Wasserman
Statistical Inference for Cluster Trees. NIPS (2016)
Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. NIPS (2016)
Christian Kroer, Miroslav Dudík, Sébastien Lahaie, Sivaraman Balakrishnan
Arbitrage-Free Combinatorial Market Making via Integer Programming. CoRR (2016)
Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright
Statistical and Computational Guarantees for the Baum-Welch Algorithm. CoRR (2015)
Nihar B. Shah, Sivaraman Balakrishnan, Joseph K. Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. AISTATS (2015)
Nihar B. Shah, Sivaraman Balakrishnan, Adityanand Guntuboyina, Martin J. Wainwright
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues. CoRR (2015)
Nihar B. Shah, Sivaraman Balakrishnan, Joseph K. Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. CoRR (2015)
Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright
Statistical and computational guarantees for the Baum-Welch algorithm. Allerton (2015)
Nihar B. Shah, Sivaraman Balakrishnan, Joseph K. Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright
When is it Better to Compare than to Score? CoRR (2014)
Sivaraman Balakrishnan, Martin J. Wainwright, Bin Yu
Statistical guarantees for the EM algorithm: From population to sample-based analysis. CoRR (2014)
Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman
Cluster Trees on Manifolds. NIPS (2013)
Sivaraman Balakrishnan, Brittany Fasy, Fabrizio Lecci, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman
Statistical Inference For Persistent Homology CoRR (2013)
Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman
Cluster Trees on Manifolds. CoRR (2013)
Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman
Tight Lower Bounds for Homology Inference. CoRR (2013)
Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh
Efficient Active Algorithms for Hierarchical Clustering CoRR (2012)
Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu
Optimal kernel choice for large-scale two-sample tests. NIPS (2012)
Sivaraman Balakrishnan, Alessandro Rinaldo, Don Sheehy, Aarti Singh, Larry A. Wasserman
Minimax rates for homology inference. AISTATS (2012)
Sivaraman Balakrishnan, Kriti Puniyani, John D. Lafferty
Sparse Additive Functional and Kernel CCA. ICML (2012)
Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh
Efficient Active Algorithms for Hierarchical Clustering. ICML (2012)
Sivaraman Balakrishnan, Kriti Puniyani, John D. Lafferty
Sparse Additive Functional and Kernel CCA CoRR (2012)
Sivaraman Balakrishnan, Alessandro Rinaldo, Don Sheehy, Aarti Singh, Larry A. Wasserman
Minimax Rates for Homology Inference CoRR (2011)
Mladen Kolar, Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh
Minimax Localization of Structural Information in Large Noisy Matrices. NIPS (2011)
Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh
Noise Thresholds for Spectral Clustering. NIPS (2011)
Sivaraman Balakrishnan, Oznur Tastan, Jaime G. Carbonell, Judith Klein-Seetharaman
Communication interception of human signal transduction pathways by Human Immunodeficiency Virus-1. GENSiPS (2009)