Sivaraman Balakrishnan

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Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityAdresse e-mail validée de stat.cmu.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityAdresse e-mail validée de cmu.edu
Martin WainwrightProfessor, EECS/Mathematics, Massachusetts Institute of TechnologyAdresse e-mail validée de mit.edu
Alessandro RinaldoThe University of Texas at AustinAdresse e-mail validée de austin.utexas.edu
Nihar B. ShahML and CS departments, Carnegie Mellon UniversityAdresse e-mail validée de cs.cmu.edu
Zachary C. LiptonProfessor in the Machine Learning Department at Carnegie Mellon; Chief Scientific Officer at AbridgeAdresse e-mail validée de cmu.edu
Pradeep RavikumarProfessor, School of Computer Science, Carnegie Mellon UniversityAdresse e-mail validée de cs.cmu.edu
Adarsh PrasadPhD Student, Machine Learning Department, Carnegie Mellon UniversityAdresse e-mail validée de andrew.cmu.edu
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonAdresse e-mail validée de cs.washington.edu
Saurabh GargPhD Student, Machine Learning Department, Carnegie Mellon UniversityAdresse e-mail validée de andrew.cmu.edu
Yining WangAssistant professor, Warrington College of Business, University of FloridaAdresse e-mail validée de warrington.ufl.edu
Fabrizio LecciColumbia UniversityAdresse e-mail validée de columbia.edu
Edward H. KennedyAssociate Professor of Statistics & Data Science, Carnegie Mellon UniversityAdresse e-mail validée de stat.cmu.edu
Brittany Terese FasyMontana State UniversityAdresse e-mail validée de fasy.us
Aaditya RamdasStatistics and Machine Learning, Carnegie Mellon UniversityAdresse e-mail validée de stat.cmu.edu
Bin YUProfessor of Statistics and EECS, UC BerkeleyAdresse e-mail validée de stat.berkeley.edu
Kannan RamchandranProfessor of Electrical Engineering and Computer Science, UC BerkeleyAdresse e-mail validée de eecs.berkeley.edu
Abhay Parekh(Ex) Adjunct Professor, Electrical Engineering and Computer Science, U C BerkeleyAdresse e-mail validée de eecs.berkeley.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstAdresse e-mail validée de cs.umass.edu
Min XuRutgers UniversityAdresse e-mail validée de stat.rutgers.edu

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Sivaraman Balakrishnan

Carnegie Mellon University

Adresse e-mail validée de stat.cmu.edu - Page d'accueil

Statistics Machine Learning


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	663	2012
Statistical guarantees for the EM algorithm: From population to sample-based analysis S Balakrishnan, MJ Wainwright, B Yu	516	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	358	2011
Confidence sets for persistence diagrams BT Fasy, F Lecci, A Rinaldo, L Wasserman, S Balakrishnan, A Singh	347*	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	220	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	188*	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	168	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	163	2016
Computationally efficient robust sparse estimation in high dimensions S Balakrishnan, SS Du, J Li, A Singh Conference on Learning Theory, 169-212, 2017	141*	2017
Noise thresholds for spectral clustering S Balakrishnan, M Xu, A Krishnamurthy, A Singh Advances in Neural Information Processing Systems, 954-962, 2011	113	2011
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	100	2018
Universal inference L Wasserman, A Ramdas, S Balakrishnan Proceedings of the National Academy of Sciences 117 (29), 16880-16890, 2020	99	2020
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	96	2020
Efficient active algorithms for hierarchical clustering A Krishnamurthy, S Balakrishnan, M Xu, A Singh arXiv preprint arXiv:1206.4672, 2012	95	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	80*	2018
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	72	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	70*	2011
Sharp instruments for classifying compliers and generalizing causal effects EH Kennedy, S Balakrishnan, M G’Sell	69	2020
Hypothesis testing for high-dimensional multinomials: A selective review S Balakrishnan, L Wasserman	66	2018
Robust multivariate nonparametric tests via projection averaging I Kim, S Balakrishnan, L Wasserman	59*	2020

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