Sivaraman Balakrishnan
Sivaraman Balakrishnan
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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, 1205-1213, 2012
Statistical guarantees for the EM algorithm: From population to sample-based analysis
S Balakrishnan, MJ Wainwright, B Yu
Annals of Statistics 45 (1), 77-120, 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
Confidence sets for persistence diagrams
BT Fasy, F Lecci, A Rinaldo, L Wasserman, S Balakrishnan, A Singh
Annals of Statistics 42 (6), 2301-2339, 2014
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
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
Noise thresholds for spectral clustering
S Balakrishnan, M Xu, A Krishnamurthy, A Singh
Advances in Neural Information Processing Systems, 954-962, 2011
Computationally efficient robust sparse estimation in high dimensions
S Balakrishnan, SS Du, J Li, A Singh
Conference on Learning Theory, 169-212, 2017
Local maxima in the likelihood of gaussian mixture models: Structural results and algorithmic consequences
C Jin, Y Zhang, S Balakrishnan, MJ Wainwright, M Jordan
arXiv preprint arXiv:1609.00978, 2016
Efficient active algorithms for hierarchical clustering
A Krishnamurthy, S Balakrishnan, M Xu, A Singh
arXiv preprint arXiv:1206.4672, 2012
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
A permutation-based model for crowd labeling: Optimal estimation and robustness
NB Shah, S Balakrishnan, MJ Wainwright
IEEE Transactions on Information Theory, 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
Cluster trees on manifolds
S Balakrishnan, S Narayanan, A Rinaldo, A Singh, L Wasserman
arXiv preprint arXiv:1307.6515, 2013
Feeling the Bern: Adaptive estimators for Bernoulli probabilities of pairwise comparisons
NB Shah, S Balakrishnan, MJ Wainwright
IEEE Transactions on Information Theory 65 (8), 4854-4874, 2019
Statistical and computational tradeoffs in biclustering
S Balakrishnan, M Kolar, A Rinaldo, A Singh, L Wasserman
NIPS 2011 workshop on computational trade-offs in statistical learning 4, 2011
Hypothesis testing for high-dimensional multinomials: A selective review
S Balakrishnan, L Wasserman
Annals of Applied Statistics 12 (2), 727-749, 2018
How many samples are needed to estimate a convolutional neural network?
SS Du, Y Wang, X Zhai, S Balakrishnan, R Salakhutdinov, A Singh
NeurIPS, 371-381, 2018
Some scaling laws for MOOC assessments
NB Shah, J Bradley, S Balakrishnan, A Parekh, K Ramchandran, ...
KDD Workshop on Data Mining for Educational Assessment and Feedback (ASSESS …, 2014
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