Ryan Tibshirani

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Robert TibshiraniProfessor of Biomedical Data Sciences, and of Statistics, Stanford UniversityVerified email at stanford.edu
Jonathan TaylorProfessor of Statistics, Stanford UniversityVerified email at stanford.edu
Trevor HastieProfessor of Statistics, Stanford UniversityVerified email at stanford.edu
Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityVerified email at stat.cmu.edu
Aaditya RamdasAssociate Professor (with tenure), Carnegie Mellon UniversityVerified email at stat.cmu.edu
Alessandro RinaldoThe University of Texas at AustinVerified email at austin.utexas.edu
Emmanuel CandesThe Simons Chair in Mathematics and Statistics, Stanford UniversityVerified email at stanford.edu
Rina Foygel BarberUniversity of ChicagoVerified email at uchicago.edu
Roni RosenfeldProfessor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Richard LockhartProfessor of Statistics, Simon Fraser UniversityVerified email at sfu.ca
Sangwon HyunUniversity of California, Santa CruzVerified email at ucsc.edu
Veeranjaneyulu SadhanalaGoogleVerified email at google.com
Max G'SellCarnegie Mellon UniversityVerified email at cmu.edu
Yu-Xiang WangAssociate Professor @ UC San DiegoVerified email at ucsd.edu
Saharon RossetTel Aviv UniversityVerified email at post.tau.ac.il
James SharpnackUC Davis Statistics DepartmentVerified email at ucdavis.edu
Jing LeiCarnegie Mellon UniversityVerified email at andrew.cmu.edu
David C. FarrowCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Alex SmolaBoson AIVerified email at smola.org
Alnur AliStanford UniversityVerified email at stanford.edu

Ryan Tibshirani

Professor of Statistics, UC Berkeley

Verified email at berkeley.edu - Homepage

Statistics Machine Learning Optimization


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
The solution path of the generalized lasso RJ Tibshirani, J Taylor Annals of Statistics 39 (3), 1335-1371, 2011	1149	2011
Distribution-free predictive inference for regression J Lei, M G’Sell, A Rinaldo, RJ Tibshirani, L Wasserman Journal of the American Statistical Association 113 (523), 1094-1111, 2018	1008	2018
Surprises in high-dimensional ridgeless least squares interpolation T Hastie, A Montanari, S Rosset, RJ Tibshirani Annals of Statistics 50 (2), 949-986, 2022	994	2022
A significance test for the lasso R Lockhart, J Taylor, RJ Tibshirani, R Tibshirani Annals of Statistics 42 (2), 413-468, 2014	899	2014
Strong rules for discarding predictors in lasso‐type problems R Tibshirani, J Bien, J Friedman, T Hastie, N Simon, J Taylor, ... Journal of the Royal Statistical Society: Series B 74 (2), 245-266, 2012	789	2012
The lasso problem and uniqueness RJ Tibshirani Electronic Journal of Statistics 7, 1456-1490, 2013	787	2013
Best subset, forward stepwise or lasso? Analysis and recommendations based on extensive comparisons T Hastie, R Tibshirani, RJ Tibshirani Statistical Science 35 (4), 579-592, 2020	702*	2020
Exact post-selection inference for sequential regression procedures RJ Tibshirani, J Taylor, R Lockhart, R Tibshirani Journal of the American Statistical Association 111 (514), 600-620, 2016	586*	2016
Adaptive piecewise polynomial estimation via trend filtering RJ Tibshirani Annals of Statistics 42 (1), 285-323, 2014	489	2014
Conformal prediction under covariate shift RJ Tibshirani, RF Barber, EJ Candes, A Ramdas Advances in Neural Information Processing Systems, 2019	488	2019
Degrees of freedom in lasso problems RJ Tibshirani, J Taylor Annals of Statistics 40 (2), 1198-1232, 2012	471	2012
Predictive inference with the jackknife+ RF Barber, EJ Candes, A Ramdas, RJ Tibshirani Annals of Statistics 49 (1), 486-507, 2021	372	2021
Trend filtering on graphs YX Wang, J Sharpnack, AJ Smola, RJ Tibshirani Journal of Machine Learning Research 17 (105), 1-41, 2016	326	2016
The limits of distribution-free conditional predictive inference R Foygel Barber, EJ Candes, A Ramdas, RJ Tibshirani Information and Inference: A Journal of the IMA 10 (2), 455-482, 2021	278	2021
Conformal prediction beyond exchangeability RF Barber, EJ Candes, A Ramdas, RJ Tibshirani Annals of Statistics 51 (2), 816-845, 2023	274	2023
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States EY Cramer, EL Ray, VK Lopez, J Bracher, A Brennen, AJC Rivadeneira, ... Proceedings of the National Academy of Sciences 119 (15), e2113561119, 2022	261	2022
An open challenge to advance probabilistic forecasting for dengue epidemics MA Johansson, KM Apfeldorf, S Dobson, J Devita, AL Buczak, B Baugher, ... Proceedings of the National Academy of Sciences 116 (48), 24268-24274, 2019	224	2019
Karush-Kuhn-Tucker conditions G Gordon, R Tibshirani Lecture notes, Carnegie Mellon University, 2012	176	2012
Nearly-isotonic regression RJ Tibshirani, H Hoefling, R Tibshirani Technometrics 53 (1), 54-61, 2011	163	2011
Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination JA Salomon, A Reinhart, A Bilinski, EJ Chua, W La Motte-Kerr, MM Rönn, ... Proceedings of the National Academy of Sciences 118 (51), e2111454118, 2021	154*	2021

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