Sivaraman Balakrishnan

97 papers A* 18A 7B 6Misc 2Journal 56Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	ShakyPrepend: A Multi-Group Learner with Improved Sample Complexity. CoRR Lujing Zhang, Daniel Hsu, Sivaraman Balakrishnan
2025	J	`jnl`	Panprediction: Optimal Predictions for Any Downstream Task and Loss. CoRR Sivaraman Balakrishnan, Nika Haghtalab, Daniel Hsu, Brian Lee, Eric Zhao
2025	J	`jnl`	Stability Bounds for Smooth Optimal Transport Maps and their Statistical Implications. CoRR Sivaraman Balakrishnan, Tudor Manole
2025	J	`jnl`	Statistical Inference for Optimal Transport Maps: Recent Advances and Perspectives. CoRR Sivaraman Balakrishnan, Tudor Manole, Larry A. Wasserman
2023	A*	`conf`	Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. NeurIPS Saurabh Garg, Amrith Setlur, Zachary C. Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
2023	J	`jnl`	Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. CoRR Saurabh Garg, Amrith Setlur, Zachary Chase Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
2023	A	`conf`	Domain Adaptation under Missingness Shift. AISTATS Helen Zhou, Sivaraman Balakrishnan, Zachary C. Lipton
2023	A*	`conf`	Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms. NeurIPS Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary C. Lipton, Yu-Xiang Wang
2023	J	`jnl`	Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms. CoRR Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary Chase Lipton, Yu-Xiang Wang
2023	A*	`conf`	RLSbench: Domain Adaptation Under Relaxed Label Shift. ICML Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton
2023	J	`jnl`	RLSbench: Domain Adaptation Under Relaxed Label Shift. CoRR Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton
2022	J	`jnl`	Domain Adaptation under Missingness Shift. CoRR Helen Zhou, Sivaraman Balakrishnan, Zachary C. Lipton
2022	A*	`conf`	Domain Adaptation under Open Set Label Shift. NeurIPS Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton
2022	J	`jnl`	Domain Adaptation under Open Set Label Shift. CoRR Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton
2022	A	`conf`	Heavy-tailed Streaming Statistical Estimation. AISTATS Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
2022	J	`jnl`	Leveraging Unlabeled Data to Predict Out-of-Distribution Performance. CoRR Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton, Behnam Neyshabur, Hanie Sedghi
2022	A*	`conf`	Leveraging unlabeled data to predict out-of-distribution performance. ICLR Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, Hanie Sedghi
2022	B	`conf`	No Rose for MLE: Inadmissibility of MLE for Evaluation Aggregation Under Levels of Expertise. ISIT Charvi Rastogi, Ivan Stelmakh, Nihar B. Shah, Sivaraman Balakrishnan
2022	J	`jnl`	Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions. J. Mach. Learn. Res. Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh
2022	B	`conf`	Understanding Simultaneous Train and Test Robustness. ALT Pranjal Awasthi, Sivaraman Balakrishnan, Aravindan Vijayaraghavan
2021	J	`jnl`	A Permutation-Based Model for Crowd Labeling: Optimal Estimation and Robustness. IEEE Trans. Inf. Theory Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
2021	J	`jnl`	Heavy-tailed Streaming Statistical Estimation. CoRR Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
2021	A	`conf`	Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs. AISTATS Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
2021	A*	`conf`	Mixture Proportion Estimation and PU Learning: A Modern Approach. NeurIPS Saurabh Garg, Yifan Wu, Alexander J. Smola, Sivaraman Balakrishnan, Zachary C. Lipton
2021	J	`jnl`	Mixture Proportion Estimation and PU Learning: A Modern Approach. CoRR Saurabh Garg, Yifan Wu, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton
2021	A*	`conf`	On Proximal Policy Optimization's Heavy-tailed Gradients. ICML Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Zachary C. Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Ravikumar
2021	J	`jnl`	On Proximal Policy Optimization's Heavy-tailed Gradients. CoRR Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Sivaraman Balakrishnan, Zachary C. Lipton, Ruslan Salakhutdinov, Pradeep Ravikumar
2021	J	`jnl`	Path Length Bounds for Gradient Descent and Flow. J. Mach. Learn. Res. Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas
2021	A*	`conf`	RATT: Leveraging Unlabeled Data to Guarantee Generalization. ICML Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary C. Lipton
2021	J	`jnl`	RATT: Leveraging Unlabeled Data to Guarantee Generalization. CoRR Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary C. Lipton
2021	J	`jnl`	Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs. J. Mach. Learn. Res. Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
2020	A	`conf`	A Robust Univariate Mean Estimator is All You Need. AISTATS Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
2020	A*	`conf`	A Unified View of Label Shift Estimation. NeurIPS Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton
2020	J	`jnl`	A Unified View of Label Shift Estimation. CoRR Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton
2020	A*	`conf`	On Learning Ising Models under Huber's Contamination Model. NeurIPS Adarsh Prasad, Vishwak Srinivasan, Sivaraman Balakrishnan, Pradeep Ravikumar
2020	J	`jnl`	Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. J. Mach. Learn. Res. Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski
2020	B	`conf`	Two-Sample Testing on Pairwise Comparison Data and the Role of Modeling Assumptions. ISIT Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh
2020	J	`jnl`	Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions. CoRR Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh
2019	J	`jnl`	A Unified Approach to Robust Mean Estimation. CoRR Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
2019	J	`jnl`	Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons. IEEE Trans. Inf. Theory Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
2019	J	`jnl`	Low Permutation-rank Matrices: Structural Properties and Noisy Completion. J. Mach. Learn. Res. Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
2019	J	`jnl`	Optimization of Smooth Functions With Noisy Observations: Local Minimax Rates. IEEE Trans. Inf. Theory Yining Wang, Sivaraman Balakrishnan, Aarti Singh
2019	J	`jnl`	Path Length Bounds for Gradient Descent and Flow. CoRR Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas
2019	J	`jnl`	Rate optimal estimation and confidence intervals for high-dimensional regression with missing covariates. J. Multivar. Anal. Yining Wang, Jialei Wang, Sivaraman Balakrishnan, Aarti Singh
2018	A*	`conf`	How Many Samples are Needed to Estimate a Convolutional Neural Network? NeurIPS Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Aarti Singh
2018	J	`jnl`	How Many Samples are Needed to Learn a Convolutional Neural Network? CoRR Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Aarti Singh
2018	Misc	`conf`	Interactive Linear Regression with Pairwise Comparisons. ACSSC Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski
2018	B	`conf`	Local White Matter Architecture Defines Functional Brain Dynamics. SMC Sivaraman Balakrishnan, Yo Joong Choe, Aarti Singh, Jean M. Vettel, Timothy D. Verstynen
2018	B	`conf`	Low Permutation-Rank Matrices: Structural Properties and Noisy Completion. ISIT Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
2018	A*	`conf`	Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. ICML Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski
2018	J	`jnl`	Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. CoRR Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski
2018	A*	`conf`	Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates. NeurIPS Yining Wang, Sivaraman Balakrishnan, Aarti Singh
2018	J	`jnl`	Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates. CoRR Yining Wang, Sivaraman Balakrishnan, Aarti Singh
2018	J	`jnl`	Robust Estimation via Robust Gradient Estimation. CoRR Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan, Pradeep Ravikumar
2018	J	`jnl`	Robust Nonparametric Regression under Huber's ε-contamination Model. CoRR Simon S. Du, Yining Wang, Sivaraman Balakrishnan, Pradeep Ravikumar, Aarti Singh
2018	A	`conf`	Stochastic Zeroth-order Optimization in High Dimensions. AISTATS Yining Wang, Simon S. Du, Sivaraman Balakrishnan, Aarti Singh
2017	Misc	`conf`	Biases in Data-Driven Networking, and What to Do About Them. HotNets Mihovil Bartulovic, Junchen Jiang, Sivaraman Balakrishnan, Vyas Sekar, Bruno Sinopoli
2017	J	`jnl`	Computationally Efficient Robust Estimation of Sparse Functionals. CoRR Simon S. Du, Sivaraman Balakrishnan, Aarti Singh
2017	A*	`conf`	Computationally Efficient Robust Sparse Estimation in High Dimensions. COLT Sivaraman Balakrishnan, Simon S. Du, Jerry Li, Aarti Singh
2017	J	`jnl`	Hypothesis Testing For Densities and High-Dimensional Multinomials: Sharp Local Minimax Rates. CoRR Sivaraman Balakrishnan, Larry A. Wasserman
2017	J	`jnl`	Hypothesis Testing for High-Dimensional Multinomials: A Selective Review. CoRR Sivaraman Balakrishnan, Larry A. Wasserman
2017	J	`jnl`	Low Permutation-rank Matrices: Structural Properties and Noisy Completion. CoRR Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
2017	J	`jnl`	Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates. CoRR Yining Wang, Jialei Wang, Sivaraman Balakrishnan, Aarti Singh
2017	J	`jnl`	Statistical and Computational Guarantees for the Baum-Welch Algorithm. J. Mach. Learn. Res. Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright
2017	J	`jnl`	Stochastic Zeroth-order Optimization in High Dimensions. CoRR Yining Wang, Simon S. Du, Sivaraman Balakrishnan, Aarti Singh
2017	J	`jnl`	Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues. IEEE Trans. Inf. Theory Nihar B. Shah, Sivaraman Balakrishnan, Adityanand Guntuboyina, Martin J. Wainwright
2016	J	`jnl`	A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness. CoRR Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
2016	A*	`conf`	Arbitrage-Free Combinatorial Market Making via Integer Programming. EC Christian Kroer, Miroslav Dudík, Sébastien Lahaie, Sivaraman Balakrishnan
2016	J	`jnl`	Arbitrage-Free Combinatorial Market Making via Integer Programming. CoRR Christian Kroer, Miroslav Dudík, Sébastien Lahaie, Sivaraman Balakrishnan
2016	J	`jnl`	Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. J. Mach. Learn. Res. Nihar B. Shah, Sivaraman Balakrishnan, Joseph K. Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright
2016	J	`jnl`	Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons. CoRR Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
2016	B	`conf`	Feeling the bern: Adaptive estimators for Bernoulli probabilities of pairwise comparisons. ISIT Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
2016	—	`conf`	Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. NIPS Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan
2016	J	`jnl`	Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. CoRR Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan
2016	—	`conf`	Statistical Inference for Cluster Trees. NIPS Jisu Kim, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry A. Wasserman
2016	A*	`conf`	Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues. ICML Nihar B. Shah, Sivaraman Balakrishnan, Aditya Guntuboyina, Martin J. Wainwright
2015	A	`conf`	Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. AISTATS Nihar B. Shah, Sivaraman Balakrishnan, Joseph K. Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright
2015	J	`jnl`	Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. CoRR Nihar B. Shah, Sivaraman Balakrishnan, Joseph K. Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright
2015	J	`jnl`	Statistical and Computational Guarantees for the Baum-Welch Algorithm. CoRR Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright
2015	—	`conf`	Statistical and computational guarantees for the Baum-Welch algorithm. Allerton Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright
2015	J	`jnl`	Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues. CoRR Nihar B. Shah, Sivaraman Balakrishnan, Adityanand Guntuboyina, Martin J. Wainwright
2014	J	`jnl`	Statistical guarantees for the EM algorithm: From population to sample-based analysis. CoRR Sivaraman Balakrishnan, Martin J. Wainwright, Bin Yu
2014	J	`jnl`	When is it Better to Compare than to Score? CoRR Nihar B. Shah, Sivaraman Balakrishnan, Joseph K. Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright
2013	—	`conf`	Cluster Trees on Manifolds. NIPS Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman
2013	J	`jnl`	Cluster Trees on Manifolds. CoRR Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman
2013	J	`jnl`	Statistical Inference For Persistent Homology CoRR Sivaraman Balakrishnan, Brittany Terese Fasy, Fabrizio Lecci, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman
2013	J	`jnl`	Tight Lower Bounds for Homology Inference. CoRR Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman
2012	J	`jnl`	Efficient Active Algorithms for Hierarchical Clustering CoRR Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh
2012	A*	`conf`	Efficient Active Algorithms for Hierarchical Clustering. ICML Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh
2012	A	`conf`	Minimax rates for homology inference. AISTATS Sivaraman Balakrishnan, Alessandro Rinaldo, Don Sheehy, Aarti Singh, Larry A. Wasserman
2012	—	`conf`	Optimal kernel choice for large-scale two-sample tests. NIPS Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu
2012	J	`jnl`	Sparse Additive Functional and Kernel CCA CoRR Sivaraman Balakrishnan, Kriti Puniyani, John D. Lafferty
2012	A*	`conf`	Sparse Additive Functional and Kernel CCA. ICML Sivaraman Balakrishnan, Kriti Puniyani, John D. Lafferty
2011	—	`conf`	Minimax Localization of Structural Information in Large Noisy Matrices. NIPS Mladen Kolar, Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh
2011	J	`jnl`	Minimax Rates for Homology Inference CoRR Sivaraman Balakrishnan, Alessandro Rinaldo, Don Sheehy, Aarti Singh, Larry A. Wasserman
2011	—	`conf`	Noise Thresholds for Spectral Clustering. NIPS Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh
2009	—	`conf`	Communication interception of human signal transduction pathways by Human Immunodeficiency Virus-1. GENSiPS Sivaraman Balakrishnan, Öznur Tastan, Jaime G. Carbonell, Judith Klein-Seetharaman