Aaditya Ramdas

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Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityVerified email at stat.cmu.edu
Rina Foygel BarberUniversity of ChicagoVerified email at uchicago.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityVerified email at cmu.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Emmanuel CandesThe Simons Chair in Mathematics and Statistics, Stanford UniversityVerified email at stanford.edu
Martin WainwrightProfessor, EECS/Mathematics, Massachusetts Institute of TechnologyVerified email at mit.edu
Ryan TibshiraniProfessor of StatisticsVerified email at berkeley.edu
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Leila WehbeCarnegie Mellon UniversityVerified email at cmu.edu
Eugene KatsevichAssistant Professor, Wharton Statistics DepartmentVerified email at wharton.upenn.edu
Alessandro RinaldoCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Jaehyeok ShinGoogleVerified email at google.com
Deanna NeedellProfessor of Mathematics, UCLAVerified email at cmc.edu
Jasjeet SekhonEugene Meyer Professor of Data Science, Political Science, and Statistics, Yale UniversityVerified email at yale.edu
Jon McAuliffeProfessor (Adj.) of Statistics, University of California, BerkeleyVerified email at stat.berkeley.edu
Sivaraman BalakrishnanCarnegie Mellon UniversityVerified email at stat.cmu.edu
Arthur GrettonGatsby Computational Neuroscience Unit and DeepmindVerified email at gatsby.ucl.ac.uk
Johannes RufLondon School of EconomicsVerified email at lse.ac.uk
Wouter M. KoolenCentrum Wiskunde & Informatica; University of TwenteVerified email at cwi.nl
Ruodu WangUniversity of WaterlooVerified email at uwaterloo.ca

Aaditya Ramdas

Statistics and Machine Learning, Carnegie Mellon University

Verified email at stat.cmu.edu - Homepage

Machine Learning Statistics


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On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests A Ramdas, N Garcia, M Cuturi Entropy, Special Issue on Statistical Significance and the Logic of …, 2017	370	2017
Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses L Wehbe, B Murphy, P Talukdar, A Fyshe, A Ramdas, T Mitchell PLOS One, 2014	277	2014
Predictive inference with the jackknife+ RF Barber, EJ Candes, A Ramdas, RJ Tibshirani The Annals of Statistics, 2021	189	2021
Conformal prediction under covariate shift RJ Tibshirani, RF Barber, EJ Candès, A Ramdas Advances in Neural Information Processing Systems 32, 2019	180*	2019
Time-uniform, nonparametric, nonasymptotic confidence sequences SR Howard, A Ramdas, J McAuliffe, J Sekhon The Annals of Statistics, 2021	177	2021
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy DJ Sutherland, HY Tung, H Strathmann, S De, A Ramdas, A Smola, ... International Conference on Learning Representations (ICLR), 2017, 2016	173	2016
Convergence properties of the randomized extended Gauss-Seidel and Kaczmarz methods A Ma, D Needell, A Ramdas SIAM Journal on Matrix Analysis and Applications, 2015	165	2015
On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions A Ramdas, S Jakkam Reddi, B Póczos, A Singh, L Wasserman 29th AAAI Conference on Artificial Intelligence, 2015	161	2015
Fast Two-Sample Testing with Analytic Representations of Probability Measures K Chwialkowski, A Ramdas, D Sejdinovic, A Gretton 29th Conference on Neural Information Processing Systems, 2015	154	2015
Algorithms for graph similarity and subgraph matching D Koutra, A Parikh, A Ramdas, J Xiang Technical report, Carnegie Mellon University, 2011	143	2011
The limits of distribution-free conditional predictive inference RF Barber, EJ Candès, A Ramdas, RJ Tibshirani Information and Inference: A Journal of the IMA, 2020	131*	2020
Fast and flexible ADMM algorithms for trend filtering A Ramdas, RJ Tibshirani Journal of Computational and Graphical Statistics, 2014	128	2014
Time-uniform Chernoff bounds via nonnegative supermartingales SR Howard, A Ramdas, J McAuliffe, J Sekhon Probability Surveys, 2020	93*	2020
Asymptotic behavior of -based Laplacian regularization in semi-supervised learning A El Alaoui, X Cheng, A Ramdas, MJ Wainwright, MI Jordan 29th Annual Conference on Learning Theory, 879-906, 2016	93	2016
A unified treatment of multiple testing with prior knowledge using the p-filter A Ramdas, RF Barber, MJ Wainwright, MI Jordan The Annals of Statistics, 2019	90	2019
Universal Inference L Wasserman, A Ramdas, S Balakrishnan Proceedings of the National Academy of Sciences, 2020	86	2020
The p-filter: Multi-layer FDR control for grouped hypotheses RF Barber, A Ramdas Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2016	68*	2016
Classification accuracy as a proxy for two-sample testing I Kim, A Ramdas, A Singh, L Wasserman Annals of Statistics 49 (1), 411-434, 2021	66*	2021
Sequential Nonparametric Testing with the Law of the Iterated Logarithm A Balsubramani, A Ramdas 32nd Conference on Uncertainty in Artificial Intelligence (UAI), 2016	66	2016
Online control of the false discovery rate with decaying memory A Ramdas, F Yang, MJ Wainwright, MI Jordan Advances in Neural Information Processing Systems, 2017	63	2017

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