Sivaraman Balakrishnan

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	Wszystkie	Od 2020
Cytowania	6367	4643
h-indeks	37	32
i10-indeks	67	62

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Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityZweryfikowany adres z stat.cmu.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityZweryfikowany adres z cmu.edu
Martin WainwrightProfessor, EECS/Mathematics, Massachusetts Institute of TechnologyZweryfikowany adres z mit.edu
Alessandro RinaldoThe University of Texas at AustinZweryfikowany adres z austin.utexas.edu
Nihar B. ShahML and CS departments, Carnegie Mellon UniversityZweryfikowany adres z cs.cmu.edu
Zachary C. LiptonRaj Reddy Associate Professor of Machine Learning @ Carnegie Mellon University; CTO + CSO @ AbridgeZweryfikowany adres z cmu.edu
Saurabh GargMistral AI | Past: CMU ML PhDZweryfikowany adres z andrew.cmu.edu
Edward H. KennedyAssociate Professor of Statistics & Data Science, Carnegie Mellon UniversityZweryfikowany adres z stat.cmu.edu
Pradeep RavikumarProfessor, School of Computer Science, Carnegie Mellon UniversityZweryfikowany adres z cs.cmu.edu
Adarsh PrasadPhD Student, Machine Learning Department, Carnegie Mellon UniversityZweryfikowany adres z andrew.cmu.edu
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonZweryfikowany adres z cs.washington.edu
Yining WangAssociate Professor, Naveen Jindal School of Management, University of Texas at DallasZweryfikowany adres z warrington.ufl.edu
Fabrizio LecciColumbia UniversityZweryfikowany adres z columbia.edu
Ilmun KimYonsei UniversityZweryfikowany adres z yonsei.ac.kr
Brittany Terese FasyMontana State UniversityZweryfikowany adres z fasy.us
Aaditya RamdasAssociate Professor (with tenure), Carnegie Mellon UniversityZweryfikowany adres z stat.cmu.edu
Matey NeykovAssistant Professor, Department of Statistics and Data Science, Northwestern UniversityZweryfikowany adres z northwestern.edu
Bin YUProfessor of Statistics and EECS, UC BerkeleyZweryfikowany adres z stat.berkeley.edu
Abhay Parekh(Ex) Adjunct Professor, Electrical Engineering and Computer Science, U C BerkeleyZweryfikowany adres z eecs.berkeley.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstZweryfikowany adres z cs.umass.edu

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Sivaraman Balakrishnan

Carnegie Mellon University

Zweryfikowany adres z stat.cmu.edu - Strona główna

Statistics Machine Learning


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
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	845	2012
Statistical guarantees for the EM algorithm: From population to sample-based analysis S Balakrishnan, MJ Wainwright, B Yu	634	2017
Confidence sets for persistence diagrams BT Fasy, F Lecci, A Rinaldo, L Wasserman, S Balakrishnan, A Singh	401*	2014
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	391	2011
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	259	2020
Estimation from pairwise comparisons: Sharp minimax bounds with topology dependence NB Shah, S Balakrishnan, J Bradley, A Parekh, K Ramch, MJ Wainwright Journal of Machine Learning Research 17 (58), 1-47, 2016	223*	2016
Universal inference L Wasserman, A Ramdas, S Balakrishnan Proceedings of the National Academy of Sciences 117 (29), 16880-16890, 2020	213	2020
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	192	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	190	2016
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	179	2020
Computationally efficient robust sparse estimation in high dimensions S Balakrishnan, SS Du, J Li, A Singh Conference on Learning Theory, 169-212, 2017	164*	2017
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	156	2022
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	136	2018
Noise thresholds for spectral clustering S Balakrishnan, M Xu, A Krishnamurthy, A Singh Advances in Neural Information Processing Systems, 954-962, 2011	119	2011
Sharp instruments for classifying compliers and generalizing causal effects EH Kennedy, S Balakrishnan, M G’sell	109	2020
Plugin estimation of smooth optimal transport maps T Manole, S Balakrishnan, J Niles-Weed, L Wasserman The Annals of Statistics 52 (3), 966-998, 2024	106	2024
Efficient active algorithms for hierarchical clustering A Krishnamurthy, S Balakrishnan, M Xu, A Singh arXiv preprint arXiv:1206.4672, 2012	101	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	100*	2018
Robust multivariate nonparametric tests via projection averaging I Kim, S Balakrishnan, L Wasserman The Annals of Statistics 48 (6), 3417-3441, 2020	86*	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	81	2020

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