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Sivaraman Balakrishnan
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2020 – today
- 2023
- [c41]Helen Zhou, Sivaraman Balakrishnan, Zachary C. Lipton:
Domain Adaptation under Missingness Shift. AISTATS 2023: 9577-9606 - [c40]Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton:
RLSbench: Domain Adaptation Under Relaxed Label Shift. ICML 2023: 10879-10928 - [c39]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 - [c38]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 - [i40]Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton:
RLSbench: Domain Adaptation Under Relaxed Label Shift. CoRR abs/2302.03020 (2023) - [i39]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 abs/2305.19570 (2023) - [i38]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 abs/2312.03318 (2023) - 2022
- [j12]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: 225:1-225:48 (2022) - [c37]Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar:
Heavy-tailed Streaming Statistical Estimation. AISTATS 2022: 1251-1282 - [c36]Pranjal Awasthi, Sivaraman Balakrishnan, Aravindan Vijayaraghavan:
Understanding Simultaneous Train and Test Robustness. ALT 2022: 34-69 - [c35]Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, Hanie Sedghi:
Leveraging unlabeled data to predict out-of-distribution performance. ICLR 2022 - [c34]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: 3168-3173 - [c33]Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton:
Domain Adaptation under Open Set Label Shift. NeurIPS 2022 - [i37]Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton, Behnam Neyshabur, Hanie Sedghi:
Leveraging Unlabeled Data to Predict Out-of-Distribution Performance. CoRR abs/2201.04234 (2022) - [i36]Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton:
Domain Adaptation under Open Set Label Shift. CoRR abs/2207.13048 (2022) - [i35]Helen Zhou, Sivaraman Balakrishnan, Zachary C. Lipton:
Domain Adaptation under Missingness Shift. CoRR abs/2211.02093 (2022) - 2021
- [j11]Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas:
Path Length Bounds for Gradient Descent and Flow. J. Mach. Learn. Res. 22: 68:1-68:63 (2021) - [j10]Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani:
Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs. J. Mach. Learn. Res. 22: 247:1-247:71 (2021) - [j9]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): 4162-4184 (2021) - [c32]Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani:
Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs. AISTATS 2021: 2602-2610 - [c31]Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary C. Lipton:
RATT: Leveraging Unlabeled Data to Guarantee Generalization. ICML 2021: 3598-3609 - [c30]Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Zachary C. Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Ravikumar:
On Proximal Policy Optimization's Heavy-tailed Gradients. ICML 2021: 3610-3619 - [c29]Saurabh Garg, Yifan Wu, Alexander J. Smola, Sivaraman Balakrishnan, Zachary C. Lipton:
Mixture Proportion Estimation and PU Learning: A Modern Approach. NeurIPS 2021: 8532-8544 - [i34]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 abs/2102.10264 (2021) - [i33]Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary C. Lipton:
RATT: Leveraging Unlabeled Data to Guarantee Generalization. CoRR abs/2105.00303 (2021) - [i32]Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar:
Heavy-tailed Streaming Statistical Estimation. CoRR abs/2108.11483 (2021) - [i31]Saurabh Garg, Yifan Wu, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton:
Mixture Proportion Estimation and PU Learning: A Modern Approach. CoRR abs/2111.00980 (2021) - 2020
- [j8]Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski:
Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. J. Mach. Learn. Res. 21: 162:1-162:54 (2020) - [c28]Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar:
A Robust Univariate Mean Estimator is All You Need. AISTATS 2020: 4034-4044 - [c27]Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh:
Two-Sample Testing on Pairwise Comparison Data and the Role of Modeling Assumptions. ISIT 2020: 1271-1276 - [c26]Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton:
A Unified View of Label Shift Estimation. NeurIPS 2020 - [c25]Adarsh Prasad, Vishwak Srinivasan, Sivaraman Balakrishnan, Pradeep Ravikumar:
On Learning Ising Models under Huber's Contamination Model. NeurIPS 2020 - [i30]Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton:
A Unified View of Label Shift Estimation. CoRR abs/2003.07554 (2020) - [i29]Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh:
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions. CoRR abs/2006.11909 (2020)
2010 – 2019
- 2019
- [j7]Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright:
Low Permutation-rank Matrices: Structural Properties and Noisy Completion. J. Mach. Learn. Res. 20: 101:1-101:43 (2019) - [j6]Yining Wang
Rate optimal estimation and confidence intervals for high-dimensional regression with missing covariates. J. Multivar. Anal. 174 (2019) - [j5]Nihar B. Shah
Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons. IEEE Trans. Inf. Theory 65(8): 4854-4874 (2019) - [j4]Yining Wang
Optimization of Smooth Functions With Noisy Observations: Local Minimax Rates. IEEE Trans. Inf. Theory 65(11): 7350-7366 (2019) - [i28]Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar:
A Unified Approach to Robust Mean Estimation. CoRR abs/1907.00927 (2019) - [i27]Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas:
Path Length Bounds for Gradient Descent and Flow. CoRR abs/1908.01089 (2019) - 2018
- [c24]Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski:
Interactive Linear Regression with Pairwise Comparisons. ACSSC 2018: 636-640 - [c23]Yining Wang, Simon S. Du, Sivaraman Balakrishnan, Aarti Singh:
Stochastic Zeroth-order Optimization in High Dimensions. AISTATS 2018: 1356-1365 - [c22]Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski:
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. ICML 2018: 5469-5478 - [c21]Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright:
Low Permutation-Rank Matrices: Structural Properties and Noisy Completion. ISIT 2018: 366-370 - [c20]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: 371-381 - [c19]Yining Wang, Sivaraman Balakrishnan, Aarti Singh:
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates. NeurIPS 2018: 4343-4354 - [c18]Sivaraman Balakrishnan, Yo Joong Choe, Aarti Singh, Jean M. Vettel, Timothy D. Verstynen:
Local White Matter Architecture Defines Functional Brain Dynamics. SMC 2018: 595-602 - [i26]Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan, Pradeep Ravikumar:
Robust Estimation via Robust Gradient Estimation. CoRR abs/1802.06485 (2018) - [i25]Yining Wang, Sivaraman Balakrishnan, Aarti Singh:
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates. CoRR abs/1803.08586 (2018) - [i24]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 abs/1805.07883 (2018) - [i23]Simon S. Du, Yining Wang, Sivaraman Balakrishnan, Pradeep Ravikumar, Aarti Singh:
Robust Nonparametric Regression under Huber's ε-contamination Model. CoRR abs/1805.10406 (2018) - [i22]Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski:
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. CoRR abs/1806.03286 (2018) - 2017
- [j3]Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright:
Statistical and Computational Guarantees for the Baum-Welch Algorithm. J. Mach. Learn. Res. 18: 125:1-125:53 (2017) - [j2]Nihar B. Shah
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues. IEEE Trans. Inf. Theory 63(2): 934-959 (2017) - [c17]Sivaraman Balakrishnan, Simon S. Du, Jerry Li, Aarti Singh:
Computationally Efficient Robust Sparse Estimation in High Dimensions. COLT 2017: 169-212 - [c16]Mihovil Bartulovic, Junchen Jiang, Sivaraman Balakrishnan, Vyas Sekar, Bruno Sinopoli:
Biases in Data-Driven Networking, and What to Do About Them. HotNets 2017: 192-198 - [i21]Yining Wang, Jialei Wang, Sivaraman Balakrishnan, Aarti Singh:
Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates. CoRR abs/1702.02686 (2017) - [i20]Simon S. Du, Sivaraman Balakrishnan, Aarti Singh:
Computationally Efficient Robust Estimation of Sparse Functionals. CoRR abs/1702.07709 (2017) - [i19]Sivaraman Balakrishnan, Larry A. Wasserman:
Hypothesis Testing For Densities and High-Dimensional Multinomials: Sharp Local Minimax Rates. CoRR abs/1706.10003 (2017) - [i18]Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright:
Low Permutation-rank Matrices: Structural Properties and Noisy Completion. CoRR abs/1709.00127 (2017) - [i17]Yining Wang, Simon S. Du, Sivaraman Balakrishnan, Aarti Singh:
Stochastic Zeroth-order Optimization in High Dimensions. CoRR abs/1710.10551 (2017) - [i16]Sivaraman Balakrishnan, Larry A. Wasserman:
Hypothesis Testing for High-Dimensional Multinomials: A Selective Review. CoRR abs/1712.06120 (2017) - 2016
- [j1]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: 58:1-58:47 (2016) - [c15]Nihar B. Shah, Sivaraman Balakrishnan, Aditya Guntuboyina, Martin J. Wainwright:
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues. ICML 2016: 11-20 - [c14]Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
Feeling the bern: Adaptive estimators for Bernoulli probabilities of pairwise comparisons. ISIT 2016: 1153-1157 - [c13]Jisu Kim, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry A. Wasserman:
Statistical Inference for Cluster Trees. NIPS 2016: 1831-1839 - [c12]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: 4116-4124 - [c11]Christian Kroer, Miroslav Dudík, Sébastien Lahaie, Sivaraman Balakrishnan:
Arbitrage-Free Combinatorial Market Making via Integer Programming. EC 2016: 161-178 - [i15]Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright:
Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons. CoRR abs/1603.06881 (2016) - [i14]Christian Kroer, Miroslav Dudík, Sébastien Lahaie, Sivaraman Balakrishnan:
Arbitrage-Free Combinatorial Market Making via Integer Programming. CoRR abs/1606.02825 (2016) - [i13]Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright:
A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness. CoRR abs/1606.09632 (2016) - [i12]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 abs/1609.00978 (2016) - 2015
- [c10]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 - [c9]Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright
Statistical and computational guarantees for the Baum-Welch algorithm. Allerton 2015: 658-665 - [i11]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 abs/1505.01462 (2015) - [i10]Nihar B. Shah, Sivaraman Balakrishnan, Adityanand Guntuboyina, Martin J. Wainwright:
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues. CoRR abs/1510.05610 (2015) - [i9]Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright:
Statistical and Computational Guarantees for the Baum-Welch Algorithm. CoRR abs/1512.08269 (2015) - 2014
- [i8]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 abs/1406.6618 (2014) - [i7]Sivaraman Balakrishnan, Martin J. Wainwright, Bin Yu:
Statistical guarantees for the EM algorithm: From population to sample-based analysis. CoRR abs/1408.2156 (2014) - 2013
- [c8]Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman:
Cluster Trees on Manifolds. NIPS 2013: 2679-2687 - [i6]Sivaraman Balakrishnan, Brittany Fasy, Fabrizio Lecci, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman:
Statistical Inference For Persistent Homology. CoRR abs/1303.7117 (2013) - [i5]Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman:
Cluster Trees on Manifolds. CoRR abs/1307.6515 (2013) - [i4]Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman:
Tight Lower Bounds for Homology Inference. CoRR abs/1307.7666 (2013) - 2012
- [c7]Sivaraman Balakrishnan, Kriti Puniyani, John D. Lafferty:
Sparse Additive Functional and Kernel CCA. ICML 2012 - [c6]Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh:
Efficient Active Algorithms for Hierarchical Clustering. ICML 2012 - [c5]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: 1214-1222 - [c4]Sivaraman Balakrishnan, Alessandro Rinaldo, Don Sheehy, Aarti Singh, Larry A. Wasserman:
Minimax rates for homology inference. AISTATS 2012: 64-72 - [i3]Sivaraman Balakrishnan, Kriti Puniyani, John D. Lafferty:
Sparse Additive Functional and Kernel CCA. CoRR abs/1206.4669 (2012) - [i2]Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh:
Efficient Active Algorithms for Hierarchical Clustering. CoRR abs/1206.4672 (2012) - 2011
- [c3]Mladen Kolar, Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh:
Minimax Localization of Structural Information in Large Noisy Matrices. NIPS 2011: 909-917 - [c2]Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh:
Noise Thresholds for Spectral Clustering. NIPS 2011: 954-962 - [i1]Sivaraman Balakrishnan, Alessandro Rinaldo, Don Sheehy, Aarti Singh, Larry A. Wasserman:
Minimax Rates for Homology Inference. CoRR abs/1112.5627 (2011)
2000 – 2009
- 2009
- [c1]Sivaraman Balakrishnan, Öznur Tastan
Communication interception of human signal transduction pathways by Human Immunodeficiency Virus-1. GENSiPS 2009: 1-4
Coauthor Index
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