Aaditya Ramdas

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Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityE-mail confirmado em stat.cmu.edu
Rina Foygel BarberUniversity of ChicagoE-mail confirmado em uchicago.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyE-mail confirmado em cs.berkeley.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityE-mail confirmado em cmu.edu
Emmanuel CandesThe Simons Chair in Mathematics and Statistics, Stanford UniversityE-mail confirmado em stanford.edu
Ryan TibshiraniProfessor of Statistics, UC BerkeleyE-mail confirmado em berkeley.edu
Martin WainwrightProfessor, EECS/Mathematics, Massachusetts Institute of TechnologyE-mail confirmado em mit.edu
Ruodu WangUniversity of WaterlooE-mail confirmado em uwaterloo.ca
Chirag GuptaCarnegie Mellon UniversityE-mail confirmado em cmu.edu
Johannes RufLondon School of EconomicsE-mail confirmado em lse.ac.uk
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityE-mail confirmado em cs.cmu.edu
Leila WehbeCarnegie Mellon UniversityE-mail confirmado em cmu.edu
Alessandro RinaldoThe University of Texas at AustinE-mail confirmado em austin.utexas.edu
Eugene KatsevichAssistant Professor, Wharton Statistics DepartmentE-mail confirmado em wharton.upenn.edu
Wouter M. KoolenCentrum Wiskunde & Informatica; University of TwenteE-mail confirmado em cwi.nl
Jaehyeok ShinGoogleE-mail confirmado em google.com
Zhiwei Steven WuCarnegie Mellon UniversityE-mail confirmado em andrew.cmu.edu
Sivaraman BalakrishnanCarnegie Mellon UniversityE-mail confirmado em stat.cmu.edu
Shubhanshu ShekharUniversity of MichiganE-mail confirmado em umich.edu
Aleksandr PodkopaevCarnegie Mellon UniversityE-mail confirmado em cmu.edu

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Aaditya Ramdas

Associate Professor, Statistics and Machine Learning, Carnegie Mellon University

E-mail confirmado em stat.cmu.edu - Página inicial

Machine Learning Statistics


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
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	548	2017
Conformal prediction under covariate shift RJ Tibshirani, RF Barber, EJ Candès, A Ramdas Advances in Neural Information Processing Systems 32, 2019	434	2019
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	341	2014
Predictive inference with the jackknife+ RF Barber, EJ Candes, A Ramdas, RJ Tibshirani The Annals of Statistics, 2021	339	2021
Time-uniform, nonparametric, nonasymptotic confidence sequences SR Howard, A Ramdas, J McAuliffe, J Sekhon The Annals of Statistics, 2021	307	2021
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	244*	2020
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	230	2016
Conformal prediction beyond exchangeability RF Barber, EJ Candes, A Ramdas, RJ Tibshirani The Annals of Statistics 51 (2), 816-845, 2023	225	2023
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	220	2015
Universal Inference L Wasserman, A Ramdas, S Balakrishnan Proceedings of the National Academy of Sciences, 2020	204	2020
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	204	2015
Estimating means of bounded random variables by betting I Waudby-Smith, A Ramdas Journal of the Royal Statistical Society, Series B (Methodology), with …, 2022	187	2022
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	177	2015
Algorithms for graph similarity and subgraph matching D Koutra, A Parikh, A Ramdas, J Xiang Technical report, Carnegie Mellon University, 2011	167	2011
Fast and flexible ADMM algorithms for trend filtering A Ramdas, RJ Tibshirani Journal of Computational and Graphical Statistics, 2014	151	2014
Time-uniform Chernoff bounds via nonnegative supermartingales SR Howard, A Ramdas, J McAuliffe, J Sekhon Probability Surveys, 2020	146*	2020
Game-theoretic statistics and safe anytime-valid inference A Ramdas, P Grünwald, V Vovk, G Shafer Statistical Science, 2023	140	2023
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	118	2019
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	117	2016
Admissible anytime-valid sequential inference must rely on nonnegative martingales A Ramdas, J Ruf, M Larsson, W Koolen arXiv preprint arXiv:2009.03167, 2020	108	2020

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