Sivaraman Balakrishnan

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

Stebėti

Sivaraman Balakrishnan

Carnegie Mellon University

Patvirtintas el. paštas stat.cmu.edu - Pagrindinis puslapis

Statistics Machine Learning


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	636	2017
Confidence sets for persistence diagrams BT Fasy, F Lecci, A Rinaldo, L Wasserman, S Balakrishnan, A Singh	403*	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	395	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	263	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	230*	2016
Universal inference L Wasserman, A Ramdas, S Balakrishnan Proceedings of the National Academy of Sciences 117 (29), 16880-16890, 2020	220	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	189	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	183	2020
Computationally efficient robust sparse estimation in high dimensions S Balakrishnan, SS Du, J Li, A Singh Conference on Learning Theory, 169-212, 2017	167*	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	138	2018
Noise thresholds for spectral clustering S Balakrishnan, M Xu, A Krishnamurthy, A Singh Advances in Neural Information Processing Systems, 954-962, 2011	120	2011
Sharp instruments for classifying compliers and generalizing causal effects EH Kennedy, S Balakrishnan, M G’sell	115	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	109	2024
Efficient active algorithms for hierarchical clustering A Krishnamurthy, S Balakrishnan, M Xu, A Singh arXiv preprint arXiv:1206.4672, 2012	103	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	102*	2018
Robust multivariate nonparametric tests via projection averaging I Kim, S Balakrishnan, L Wasserman The Annals of Statistics 48 (6), 3417-3441, 2020	87*	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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