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
Ryan TibshiraniProfessor of Statistics, UC BerkeleyVerified email at berkeley.edu
Emmanuel CandesThe Simons Chair in Mathematics and Statistics, Stanford UniversityVerified email at stanford.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityVerified email at cmu.edu
Martin WainwrightProfessor, EECS/Mathematics, Massachusetts Institute of TechnologyVerified email at mit.edu
Ruodu WangUniversity of WaterlooVerified email at uwaterloo.ca
Chirag GuptaCarnegie Mellon UniversityVerified email at cmu.edu
Alessandro RinaldoThe University of Texas at AustinVerified email at austin.utexas.edu
Johannes RufLondon School of EconomicsVerified email at lse.ac.uk
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
Zhiwei Steven WuCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Jaehyeok ShinGoogleVerified email at google.com
Shubhanshu ShekharUniversity of MichiganVerified email at umich.edu
Sivaraman BalakrishnanCarnegie Mellon UniversityVerified email at stat.cmu.edu
Aleksandr PodkopaevCarnegie Mellon UniversityVerified email at cmu.edu
Jasjeet SekhonEugene Meyer Professor of Data Science, Political Science, and Statistics, Yale UniversityVerified email at yale.edu

Aaditya Ramdas

Associate Professor (with tenure), Carnegie Mellon University

Verified email at stat.cmu.edu - Homepage

Machine Learning Statistics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	594	2017
Conformal prediction under covariate shift RJ Tibshirani, RF Barber, EJ Candès, A Ramdas Advances in Neural Information Processing Systems 32, 2019	488	2019
Predictive inference with the jackknife+ RF Barber, EJ Candes, A Ramdas, RJ Tibshirani The Annals of Statistics, 2021	372	2021
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	347	2014
Time-uniform, nonparametric, nonasymptotic confidence sequences SR Howard, A Ramdas, J McAuliffe, J Sekhon The Annals of Statistics, 2021	330	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	278*	2020
Conformal prediction beyond exchangeability RF Barber, EJ Candes, A Ramdas, RJ Tibshirani The Annals of Statistics 51 (2), 816-845, 2023	276	2023
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	249	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	228	2015
Universal Inference L Wasserman, A Ramdas, S Balakrishnan Proceedings of the National Academy of Sciences, 2020	224	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	213	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	210	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	185	2015
Game-theoretic statistics and safe anytime-valid inference A Ramdas, P Grünwald, V Vovk, G Shafer Statistical Science, 2023	174	2023
Algorithms for graph similarity and subgraph matching D Koutra, A Parikh, A Ramdas, J Xiang Technical report, Carnegie Mellon University, 2011	169	2011
Time-uniform Chernoff bounds via nonnegative supermartingales SR Howard, A Ramdas, J McAuliffe, J Sekhon Probability Surveys, 2020	160*	2020
Fast and flexible ADMM algorithms for trend filtering A Ramdas, RJ Tibshirani Journal of Computational and Graphical Statistics, 2014	154	2014
False discovery rate control with e-values R Wang, A Ramdas Journal of the Royal Statistical Society: Series B, 2022	130	2022
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	123	2019
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	120	2021

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