Sivaraman Balakrishnan

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	Tutte	Dal 2020
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Indice H	37	32
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Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityEmail verificata su stat.cmu.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityEmail verificata su cmu.edu
Martin WainwrightProfessor, EECS/Mathematics, Massachusetts Institute of TechnologyEmail verificata su mit.edu
Alessandro RinaldoThe University of Texas at AustinEmail verificata su austin.utexas.edu
Nihar B. ShahML and CS departments, Carnegie Mellon UniversityEmail verificata su cs.cmu.edu
Zachary C. LiptonRaj Reddy Associate Professor of Machine Learning @ Carnegie Mellon University; CTO + CSO @ AbridgeEmail verificata su cmu.edu
Saurabh GargMistral AI | Past: CMU ML PhDEmail verificata su andrew.cmu.edu
Edward H. KennedyAssociate Professor of Statistics & Data Science, Carnegie Mellon UniversityEmail verificata su stat.cmu.edu
Pradeep RavikumarProfessor, School of Computer Science, Carnegie Mellon UniversityEmail verificata su cs.cmu.edu
Adarsh PrasadPhD Student, Machine Learning Department, Carnegie Mellon UniversityEmail verificata su andrew.cmu.edu
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonEmail verificata su cs.washington.edu
Yining WangAssociate Professor, Naveen Jindal School of Management, University of Texas at DallasEmail verificata su warrington.ufl.edu
Fabrizio LecciColumbia UniversityEmail verificata su columbia.edu
Ilmun KimYonsei UniversityEmail verificata su yonsei.ac.kr
Brittany Terese FasyMontana State UniversityEmail verificata su fasy.us
Aaditya RamdasAssociate Professor (with tenure), Carnegie Mellon UniversityEmail verificata su stat.cmu.edu
Matey NeykovAssistant Professor, Department of Statistics and Data Science, Northwestern UniversityEmail verificata su northwestern.edu
Bin YUProfessor of Statistics and EECS, UC BerkeleyEmail verificata su stat.berkeley.edu
Abhay Parekh(Ex) Adjunct Professor, Electrical Engineering and Computer Science, U C BerkeleyEmail verificata su eecs.berkeley.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstEmail verificata su cs.umass.edu

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Sivaraman Balakrishnan

Carnegie Mellon University

Email verificata su stat.cmu.edu - Home page

Statistics Machine Learning


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	844	2012
Statistical guarantees for the EM algorithm: From population to sample-based analysis S Balakrishnan, MJ Wainwright, B Yu	633	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	222*	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	176	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	108	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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