Sivaraman Balakrishnan

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Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityGeverifieerd e-mailadres voor stat.cmu.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityGeverifieerd e-mailadres voor cmu.edu
Martin WainwrightProfessor, EECS/Mathematics, Massachusetts Institute of TechnologyGeverifieerd e-mailadres voor mit.edu
Alessandro RinaldoThe University of Texas at AustinGeverifieerd e-mailadres voor austin.utexas.edu
Nihar B. ShahML and CS departments, Carnegie Mellon UniversityGeverifieerd e-mailadres voor cs.cmu.edu
Zachary C. LiptonProfessor of Machine Learning at Carnegie Mellon; CTO / CSO at AbridgeGeverifieerd e-mailadres voor cmu.edu
Pradeep RavikumarProfessor, School of Computer Science, Carnegie Mellon UniversityGeverifieerd e-mailadres voor cs.cmu.edu
Adarsh PrasadPhD Student, Machine Learning Department, Carnegie Mellon UniversityGeverifieerd e-mailadres voor andrew.cmu.edu
Saurabh GargPhD Student, Machine Learning Department, Carnegie Mellon UniversityGeverifieerd e-mailadres voor andrew.cmu.edu
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonGeverifieerd e-mailadres voor cs.washington.edu
Yining WangAssistant professor, Warrington College of Business, University of FloridaGeverifieerd e-mailadres voor warrington.ufl.edu
Fabrizio LecciColumbia UniversityGeverifieerd e-mailadres voor columbia.edu
Edward H. KennedyAssociate Professor of Statistics & Data Science, Carnegie Mellon UniversityGeverifieerd e-mailadres voor stat.cmu.edu
Brittany Terese FasyMontana State UniversityGeverifieerd e-mailadres voor fasy.us
Aaditya RamdasStatistics and Machine Learning, Carnegie Mellon UniversityGeverifieerd e-mailadres voor stat.cmu.edu
Bin YUProfessor of Statistics and EECS, UC BerkeleyGeverifieerd e-mailadres voor stat.berkeley.edu
Kannan RamchandranProfessor of Electrical Engineering and Computer Science, UC BerkeleyGeverifieerd e-mailadres voor eecs.berkeley.edu
Abhay Parekh(Ex) Adjunct Professor, Electrical Engineering and Computer Science, U C BerkeleyGeverifieerd e-mailadres voor eecs.berkeley.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstGeverifieerd e-mailadres voor cs.umass.edu
Min XuRutgers UniversityGeverifieerd e-mailadres voor stat.rutgers.edu

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Sivaraman Balakrishnan

Carnegie Mellon University

Geverifieerd e-mailadres voor stat.cmu.edu - Homepage

Statistics Machine Learning


Titel Sorteren op citaties Sorteren op jaar Sorteren op titel	Geciteerd door Geciteerd door	Jaar
Optimal kernel choice for large-scale two-sample tests A Gretton, D Sejdinovic, H Strathmann, S Balakrishnan, M Pontil, ... Advances in neural information processing systems 25, 2012	689	2012
Statistical guarantees for the EM algorithm: From population to sample-based analysis S Balakrishnan, MJ Wainwright, B Yu	534	2017
Learning generative models for protein fold families S Balakrishnan, H Kamisetty, JG Carbonell, SI Lee, CJ Langmead Proteins: Structure, Function, and Bioinformatics 79 (4), 1061-1078, 2011	363	2011
Confidence sets for persistence diagrams BT Fasy, F Lecci, A Rinaldo, L Wasserman, S Balakrishnan, A Singh	357*	2014
Robust estimation via robust gradient estimation A Prasad, AS Suggala, S Balakrishnan, P Ravikumar Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2020	227	2020
Estimation from pairwise comparisons: Sharp minimax bounds with topology dependence N Shah, S Balakrishnan, J Bradley, A Parekh, K Ramchandran, ... Artificial intelligence and statistics, 856-865, 2015	195*	2015
Stochastically transitive models for pairwise comparisons: Statistical and computational issues N Shah, S Balakrishnan, A Guntuboyina, M Wainwright International Conference on Machine Learning, 11-20, 2016	170	2016
Local maxima in the likelihood of gaussian mixture models: Structural results and algorithmic consequences C Jin, Y Zhang, S Balakrishnan, MJ Wainwright, MI Jordan Advances in neural information processing systems 29, 2016	165	2016
Computationally efficient robust sparse estimation in high dimensions S Balakrishnan, SS Du, J Li, A Singh Conference on Learning Theory, 169-212, 2017	145*	2017
Universal inference L Wasserman, A Ramdas, S Balakrishnan Proceedings of the National Academy of Sciences 117 (29), 16880-16890, 2020	115	2020
Noise thresholds for spectral clustering S Balakrishnan, M Xu, A Krishnamurthy, A Singh Advances in Neural Information Processing Systems, 954-962, 2011	114	2011
A unified view of label shift estimation S Garg, Y Wu, S Balakrishnan, Z Lipton Advances in Neural Information Processing Systems 33, 3290-3300, 2020	107	2020
Stochastic zeroth-order optimization in high dimensions Y Wang, S Du, S Balakrishnan, A Singh International conference on artificial intelligence and statistics, 1356-1365, 2018	106	2018
Efficient active algorithms for hierarchical clustering A Krishnamurthy, S Balakrishnan, M Xu, A Singh arXiv preprint arXiv:1206.4672, 2012	98	2012
How many samples are needed to estimate a convolutional neural network? SS Du, Y Wang, X Zhai, S Balakrishnan, RR Salakhutdinov, A Singh Advances in Neural Information Processing Systems 31, 2018	83*	2018
Sharp instruments for classifying compliers and generalizing causal effects EH Kennedy, S Balakrishnan, M G’Sell	75	2020
A permutation-based model for crowd labeling: Optimal estimation and robustness NB Shah, S Balakrishnan, MJ Wainwright IEEE Transactions on Information Theory 67 (6), 4162-4184, 2020	74	2020
Minimax localization of structural information in large noisy matrices S Balakrishnan, M Kolar, A Rinaldo, A Singh Advances in Neural Information Processing Systems, 909-917, 2011	71*	2011
Hypothesis testing for high-dimensional multinomials: A selective review S Balakrishnan, L Wasserman	67	2018
Leveraging unlabeled data to predict out-of-distribution performance S Garg, S Balakrishnan, ZC Lipton, B Neyshabur, H Sedghi arXiv preprint arXiv:2201.04234, 2022	65	2022

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