Sivaraman Balakrishnan
Sivaraman Balakrishnan
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Cited by
Cited by
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
Statistical guarantees for the EM algorithm: From population to sample-based analysis
S Balakrishnan, MJ Wainwright, B Yu
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
Confidence sets for persistence diagrams
BT Fasy, F Lecci, A Rinaldo, L Wasserman, S Balakrishnan, A Singh
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
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
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
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
Computationally efficient robust sparse estimation in high dimensions
S Balakrishnan, SS Du, J Li, A Singh
Conference on Learning Theory, 169-212, 2017
Noise thresholds for spectral clustering
S Balakrishnan, M Xu, A Krishnamurthy, A Singh
Advances in Neural Information Processing Systems, 954-962, 2011
Universal inference
L Wasserman, A Ramdas, S Balakrishnan
Proceedings of the National Academy of Sciences 117 (29), 16880-16890, 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
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
Efficient active algorithms for hierarchical clustering
A Krishnamurthy, S Balakrishnan, M Xu, A Singh
arXiv preprint arXiv:1206.4672, 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
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
Sharp instruments for classifying compliers and generalizing causal effects
EH Kennedy, S Balakrishnan, M G’Sell
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
Hypothesis testing for high-dimensional multinomials: A selective review
S Balakrishnan, L Wasserman
Robust multivariate nonparametric tests via projection averaging
I Kim, S Balakrishnan, L Wasserman
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