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RATT: Leveraging Unlabeled Data to Guarantee Generalization


May 01, 2021
Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary C. Lipton

* Pre-print 

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On Proximal Policy Optimization's Heavy-tailed Gradients


Feb 20, 2021
Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Sivaraman Balakrishnan, Zachary C. Lipton, Ruslan Salakhutdinov, Pradeep Ravikumar

* Pre-print 

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Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions


Jun 21, 2020
Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh

* 36 pages, 4 figures 

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A Unified View of Label Shift Estimation


Mar 17, 2020
Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton

* Pre-print 

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Universal Inference Using the Split Likelihood Ratio Test


Feb 04, 2020
Larry Wasserman, Aaditya Ramdas, Sivaraman Balakrishnan


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Gaussian Mixture Clustering Using Relative Tests of Fit


Oct 07, 2019
Purvasha Chakravarti, Sivaraman Balakrishnan, Larry Wasserman


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Minimax Confidence Intervals for the Sliced Wasserstein Distance


Sep 17, 2019
Tudor Manole, Sivaraman Balakrishnan, Larry Wasserman


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Path Length Bounds for Gradient Descent and Flow


Aug 02, 2019
Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas

* 47 pages, 7 figures 

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A Unified Approach to Robust Mean Estimation


Jul 01, 2019
Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar

* 51 pages, 6 figures 

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Local White Matter Architecture Defines Functional Brain Dynamics


Sep 16, 2018
Yo Joong Choe, Sivaraman Balakrishnan, Aarti Singh, Jean M. Vettel, Timothy Verstynen

* Accepted to the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2018) 

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Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information


Jun 08, 2018
Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski

* 21 pages, 5 figures; International Conference on Machine Learning 2018 

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Robust Nonparametric Regression under Huber's $ε$-contamination Model


May 26, 2018
Simon S. Du, Yining Wang, Sivaraman Balakrishnan, Pradeep Ravikumar, Aarti Singh


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How Many Samples are Needed to Learn a Convolutional Neural Network?


May 21, 2018
Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Aarti Singh


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Robust Estimation via Robust Gradient Estimation


Apr 20, 2018
Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan, Pradeep Ravikumar

* 48 pages, 5 figures 

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Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates


Mar 22, 2018
Yining Wang, Sivaraman Balakrishnan, Aarti Singh

* 29 pages, 1 figure 

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Stochastic Zeroth-order Optimization in High Dimensions


Feb 26, 2018
Yining Wang, Simon Du, Sivaraman Balakrishnan, Aarti Singh

* Camera-ready version at AISTATS 2018 

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Hypothesis Testing for High-Dimensional Multinomials: A Selective Review


Dec 17, 2017
Sivaraman Balakrishnan, Larry Wasserman

* 19 pages, 6 figures. Written in memory of Stephen E. Fienberg 

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Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates


Nov 03, 2017
Yining Wang, Jialei Wang, Sivaraman Balakrishnan, Aarti Singh

* 41 pages, 1 figure, 3 tables 

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Hypothesis Testing For Densities and High-Dimensional Multinomials: Sharp Local Minimax Rates


Jun 30, 2017
Sivaraman Balakrishnan, Larry Wasserman

* 60 pages, 6 figures 

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Computationally Efficient Robust Estimation of Sparse Functionals


Feb 24, 2017
Simon S. Du, Sivaraman Balakrishnan, Aarti Singh


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Statistical Inference for Cluster Trees


Feb 12, 2017
Jisu Kim, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry Wasserman

* 20 pages, 6 figures, accepted in Neural Information Processing Systems (NIPS) 2016 

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Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues


Sep 28, 2016
Nihar B. Shah, Sivaraman Balakrishnan, Adityanand Guntuboyina, Martin J. Wainwright


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Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences


Sep 04, 2016
Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael Jordan

* Neural Information Processing Systems (NIPS) 2016 

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A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness


Jun 30, 2016
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright


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Arbitrage-Free Combinatorial Market Making via Integer Programming


Jun 10, 2016
Christian Kroer, Miroslav Dudík, Sébastien Lahaie, Sivaraman Balakrishnan


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Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons


Mar 22, 2016
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright


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Statistical and Computational Guarantees for the Baum-Welch Algorithm


Dec 27, 2015
Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright


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Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence


May 06, 2015
Nihar B. Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright

* 39 pages, 5 figures. Significant extension of arXiv:1406.6618 

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Confidence sets for persistence diagrams


Nov 20, 2014
Brittany Terese Fasy, Fabrizio Lecci, Alessandro Rinaldo, Larry Wasserman, Sivaraman Balakrishnan, Aarti Singh

* Annals of Statistics 2014, Vol. 42, No. 6, 2301-2339 
* Published in at http://dx.doi.org/10.1214/14-AOS1252 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

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