Aaditya Ramdas

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Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityDirección de correo verificada de stat.cmu.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityDirección de correo verificada de cmu.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyDirección de correo verificada de cs.berkeley.edu
Rina Foygel BarberUniversity of ChicagoDirección de correo verificada de uchicago.edu
Martin WainwrightProfessor of EECS and Statistics, UC BerkeleyDirección de correo verificada de berkeley.edu
Emmanuel CandesThe Simons Chair in Mathematics and Statistics, Stanford UniversityDirección de correo verificada de stanford.edu
Ryan TibshiraniProfessor of StatisticsDirección de correo verificada de berkeley.edu
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityDirección de correo verificada de cs.cmu.edu
Leila WehbeCarnegie Mellon UniversityDirección de correo verificada de cmu.edu
Eugene KatsevichAssistant Professor, Wharton Statistics DepartmentDirección de correo verificada de wharton.upenn.edu
Alessandro RinaldoCarnegie Mellon UniversityDirección de correo verificada de andrew.cmu.edu
Deanna NeedellProfessor of Mathematics, UCLADirección de correo verificada de cmc.edu
Jaehyeok ShinGoogleDirección de correo verificada de google.com
Jasjeet SekhonEugene Meyer Professor of Data Science, Political Science, and Statistics, Yale UniversityDirección de correo verificada de yale.edu
Jon McAuliffeProfessor (Adj.) of Statistics, University of California, BerkeleyDirección de correo verificada de stat.berkeley.edu
Arthur GrettonGatsby Computational Neuroscience Unit, UCLDirección de correo verificada de gatsby.ucl.ac.uk
Sivaraman BalakrishnanCarnegie Mellon UniversityDirección de correo verificada de stat.cmu.edu
Johannes RufLondon School of EconomicsDirección de correo verificada de lse.ac.uk
Wouter M. KoolenCentrum Wiskunde & Informatica; University of TwenteDirección de correo verificada de cwi.nl
Ruodu WangUniversity of WaterlooDirección de correo verificada de uwaterloo.ca

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Aaditya Ramdas

Statistics and Machine Learning, Carnegie Mellon University

Dirección de correo verificada de stat.cmu.edu - Página principal

Machine Learning Statistics


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	352	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	263	2014
Predictive inference with the jackknife+ RF Barber, EJ Candes, A Ramdas, RJ Tibshirani The Annals of Statistics, 2021	169	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	168	2016
Time-uniform, nonparametric, nonasymptotic confidence sequences SR Howard, A Ramdas, J McAuliffe, J Sekhon The Annals of Statistics, 2021	159	2021
Conformal prediction under covariate shift RJ Tibshirani, RF Barber, EJ Candès, A Ramdas Advances in Neural Information Processing Systems 32, 2019	155*	2019
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	155	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	152	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	147	2015
Algorithms for graph similarity and subgraph matching D Koutra, A Parikh, A Ramdas, J Xiang Technical report, Carnegie Mellon University, 2011	141	2011
Fast and flexible ADMM algorithms for trend filtering A Ramdas, RJ Tibshirani Journal of Computational and Graphical Statistics, 2014	122	2014
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	121*	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	90	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	85	2019
Time-uniform Chernoff bounds via nonnegative supermartingales SR Howard, A Ramdas, J McAuliffe, J Sekhon Probability Surveys, 2020	82*	2020
Universal Inference L Wasserman, A Ramdas, S Balakrishnan Proceedings of the National Academy of Sciences, 2020	74	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	66*	2016
Sequential Nonparametric Testing with the Law of the Iterated Logarithm A Balsubramani, A Ramdas 32nd Conference on Uncertainty in Artificial Intelligence (UAI), 2016	62	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	60	2017
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	58*	2021

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