Aaditya Ramdas

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Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityZweryfikowany adres z stat.cmu.edu
Rina Foygel BarberUniversity of ChicagoZweryfikowany adres z uchicago.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyZweryfikowany adres z cs.berkeley.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityZweryfikowany adres z cmu.edu
Emmanuel CandesThe Simons Chair in Mathematics and Statistics, Stanford UniversityZweryfikowany adres z stanford.edu
Ryan TibshiraniProfessor of Statistics, UC BerkeleyZweryfikowany adres z berkeley.edu
Martin WainwrightProfessor, EECS/Mathematics, Massachusetts Institute of TechnologyZweryfikowany adres z mit.edu
Chirag GuptaCarnegie Mellon UniversityZweryfikowany adres z cmu.edu
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityZweryfikowany adres z cs.cmu.edu
Johannes RufLondon School of EconomicsZweryfikowany adres z lse.ac.uk
Leila WehbeCarnegie Mellon UniversityZweryfikowany adres z cmu.edu
Alessandro RinaldoThe University of Texas at AustinZweryfikowany adres z austin.utexas.edu
Ruodu WangUniversity of WaterlooZweryfikowany adres z uwaterloo.ca
Eugene KatsevichAssistant Professor, Wharton Statistics DepartmentZweryfikowany adres z wharton.upenn.edu
Jaehyeok ShinGoogleZweryfikowany adres z google.com
Wouter M. KoolenCentrum Wiskunde & Informatica; University of TwenteZweryfikowany adres z cwi.nl
Zhiwei Steven WuCarnegie Mellon UniversityZweryfikowany adres z andrew.cmu.edu
Sivaraman BalakrishnanCarnegie Mellon UniversityZweryfikowany adres z stat.cmu.edu
Shubhanshu ShekharUniversity of MichiganZweryfikowany adres z umich.edu
Deanna NeedellProfessor of Mathematics, UCLAZweryfikowany adres z cmc.edu

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Aaditya Ramdas

Statistics and Machine Learning, Carnegie Mellon University

Zweryfikowany adres z stat.cmu.edu - Strona główna

Machine Learning Statistics


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
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	517	2017
Conformal prediction under covariate shift RJ Tibshirani, RF Barber, EJ Candès, A Ramdas Advances in Neural Information Processing Systems 32, 2019	382	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	332	2014
Predictive inference with the jackknife+ RF Barber, EJ Candes, A Ramdas, RJ Tibshirani The Annals of Statistics, 2021	317	2021
Time-uniform, nonparametric, nonasymptotic confidence sequences SR Howard, A Ramdas, J McAuliffe, J Sekhon The Annals of Statistics, 2021	283	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	227*	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	218	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	208	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	200	2015
Conformal prediction beyond exchangeability RF Barber, EJ Candes, A Ramdas, RJ Tibshirani The Annals of Statistics 51 (2), 816-845, 2023	187	2023
Universal Inference L Wasserman, A Ramdas, S Balakrishnan Proceedings of the National Academy of Sciences, 2020	173	2020
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	171	2015
Algorithms for graph similarity and subgraph matching D Koutra, A Parikh, A Ramdas, J Xiang Technical report, Carnegie Mellon University, 2011	164	2011
Estimating means of bounded random variables by betting I Waudby-Smith, A Ramdas Journal of the Royal Statistical Society, Series B (Methodology), with …, 2022	149	2022
Fast and flexible ADMM algorithms for trend filtering A Ramdas, RJ Tibshirani Journal of Computational and Graphical Statistics, 2014	148	2014
Time-uniform Chernoff bounds via nonnegative supermartingales SR Howard, A Ramdas, J McAuliffe, J Sekhon Probability Surveys, 2020	137*	2020
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	111	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	111	2016
Game-theoretic statistics and safe anytime-valid inference A Ramdas, P Grünwald, V Vovk, G Shafer Statistical Science, 2023	104	2023
Nested conformal prediction and quantile out-of-bag ensemble methods C Gupta, AK Kuchibhotla, AK Ramdas Pattern Recognition, Special Issue on Conformal Prediction and Applications, 2021	101	2021

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