Research papers

2022

• Rasool Fakoor, Taesup Kim, Jonas Mueller, Alex Smola, and Ryan Tibshirani. Flexible Model Aggregation for Quantile Regression.
• Rina Foygel Barber, Emmanuel Candes, Aaditya Ramdas, and Ryan Tibshirani. Conformal Prediction Beyond Exchangeability.

2021

• Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Addison Hu, and Ryan Tibshirani. Multivariate Trend Filtering for Lattice Data. [Supplement]
• Maria Jahja, Andrew Chin, and Ryan Tibshirani. Real-Time Estimation of COVID-19 Infections: Deconvolution and Sensor Fusion. Statistical Science, Vol. 37, No. 2, 207-228, 2022.
• Aaron Rumack, Ryan Tibshirani, and Roni Rosenfeld. Recalibrating Probabilistic Forecasts of Epidemics.
• Roni Rosenfeld and Ryan Tibshirani. Epidemic Tracking and Forecasting: Lessons Learned From a Tumultuous Year. Proceedings of the National Academy of Sciences, Vol. 118, No. 51, e2111456118, 2021.
• Alex Reinhart, Logan Brooks, Maria Jahja, Aaron Rumack, Jingjing Tang, [60 more authors], Roni Rosenfeld, and Ryan Tibshirani. An Open Repository of Real-Time COVID-19 Indicators. Proceedings of the National Academy of Sciences, Vol. 118, No. 51, e2111452118, 2021. [Supplement]
• Daniel J. McDonald, Jacob Bien, Alden Green, Addison J. Hu, Nat DeFries, Sangwon Hyun, Natalia L. Oliveira, James Sharpnack, Jingjing Tang, Robert Tibshirani, Valerie Ventura, Larry Wasserman, and Ryan Tibshirani. Can Auxiliary Indicators Improve COVID-19 Forecasting and Hotspot Prediction? Proceedings of the National Academy of Sciences, Vol. 118, No. 51, e2111453118, 2021. [Supplement]
• Joshua A. Salomon, Alex Reinhart, Alyssa Bilinski, Eu Jing Chua, Wichada La Motte-Kerr, Minttu M. Ronn, Marissa Reitsma, Katherine Ann Morris, Sarah LaRocca, Tamer Farag, Frauke Kreuter, Roni Rosenfeld, and Ryan Tibshirani. Continuous Real-Time Measurement of COVID-19 Symptoms, Risks, Protective Behaviors, Testing and Vaccination. Proceedings of the National Academy of Sciences, Vol. 118, No. 51, e2111454118, 2021.
• Natalia Oliveira, Jing Lei, and Ryan Tibshirani. Unbiased Risk Estimation in the Normal Means Problem via Coupled Bootstrap Techniques.
• Pratik Patil, Alessandro Rinaldo, and Ryan Tibshirani. Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression. International Conference on Artificial Intelligence and Statistics, 2022. [Supplement]
• Alden Green, Sivaraman Balakrishnan, and Ryan Tibshirani. Minimax Optimal Regression over Sobolev Spaces via Laplacian Eigenmaps on Neighborhood Graphs.
• Ryan Tibshirani. Equivalences Between Sparse Models and Neural Networks. Working notes.

2020

• Pratik Patil, Yuting Wei, Alessandro Rinaldo, and Ryan Tibshirani. Uniform Consistency of Cross-Validation for High-Dimensional Ridge Regression. International Conference on Artificial Intelligence and Statistics, 2021. [Supplement]
• Alden Green, Sivaraman Balakrishnan, and Ryan Tibshirani. Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs. International Conference on Artificial Intelligence and Statistics, 2021. [Supplement]
• Alnur Ali, Edgar Dobriban, and Ryan Tibshirani. The Implicit Regularization of Stochastic Gradient Flow for Least Squares. International Conference on Machine Learning, 2020. [Supplement]
• Ryan Tibshirani. Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems. Foundations and Trends in Machine Learning, Vol. 15, No. 6, 694-846, 2022. [R package]

2019

• Benjamin Stokell, Rajen Shah, and Ryan Tibshirani. Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties. Journal of the Royal Statistical Society: Series B, Vol. 83, No. 3, 579-611, 2021. [Supplement]
• Alden Green, Sivaraman Balakrishnan, and Ryan Tibshirani. Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs. Journal of Machine Learning Research, Vol. 22, No. 247, 1-41, 2021.
• Maria Jahja, David Farrow, Roni Rosenfeld, and Ryan Tibshirani. Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights. Neural Information Processing Systems. [Supplement]
• Rina Foygel Barber, Emmanuel Candes, Aaditya Ramdas, and Ryan Tibshirani. Conformal Prediction Under Covariate Shift. Neural Information Processing Systems. [Supplement]
• Trevor Hastie, Andrea Montanari, Saharon Rosset, and Ryan Tibshirani. Surprises in High-Dimensional Ridgeless Least Squares Interpolation. Annals of Statistics, Vol. 50, No. 2, 949-986, 2022.
• Rina Foygel Barber, Emmanuel Candes, Aaditya Ramdas, and Ryan Tibshirani. The Limits of Distribution-Free Conditional Predictive Inference. Information and Inference, Vol. 10, No. 2, 455-482, 2021.
• Rina Foygel Barber, Emmanuel Candes, Aaditya Ramdas, and Ryan Tibshirani. Predictive Inference with the Jackknife+. Annals of Statistics, Vol. 49, No. 1, 486-507, 2021.

2018

• Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, and Ryan Tibshirani. A Higher-Order Kolmogorov Smirnov Test. International Conference on Artificial Intelligence and Statistics, 2019. [Supplement]
• Alnur Ali, Zico Kolter, and Ryan Tibshirani. A Continuous-Time View of Early Stopping for Least Squares Regression. International Conference on Artificial Intelligence and Statistics, 2019. [Supplement]
• Logan Brooks, David Farrow, Sangwon Hyun, Ryan Tibshirani, and Roni Rosenfeld. Nonmechanistic Forecasts of Seasonal Influenza with Iterative One-Week-Ahead Distributions. PLOS Computational Biology, Vol. 14, No. 6, 1-29, 2018. [Supplement]
• Alnur Ali and Ryan Tibshirani. The Generalized Lasso Problem and Uniqueness. Electronic Journal of Statistics, Vol. 13, No. 2, 2307-2347, 2019.
• Sangwon Hyun, Kevin Lin, Max G'Sell, and Ryan Tibshirani. Post-Selection Inference for Changepoint Detection Algorithms with Application to Copy Number Variation Data. Biometrics, 1-13, 2021. [Supplement]

2017

• Trevor Hastie, Robert Tibshirani, and Ryan Tibshirani. Best Subset, Forward Stepwise, or Lasso? Analysis and Recommendations Based on Extensive Comparisons. Statistical Science, Vol. 35, No. 4, 579-592, 2020. [Supplement] [R package]
  • Discussion: Chen, Taeb, and Buhlmann
  • Discussion: George
  • Discussion: Mazumder
  • Discussion: Sarwar, Sauk, and Sahinidis
  • Rejoinder: Hastie, Tibshirani, and Tibshirani
• Saharon Rosset and Ryan Tibshirani. From Fixed-X to Random-X Regression: Bias-Variance Decompositions, Covariance Penalties, and Prediction Error Estimation. Journal of the American Statistical Association, Vol. 15, No. 529, 138-151, 2020.
• Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James Sharpnack, and Ryan Tibshirani. Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods. Neural Information Processing Systems, 2017. [Supplement]
• Kevin Lin, James Sharpnack, Alessandro Rinaldo, and Ryan Tibshirani. A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening. Neural Information Processing Systems, 2017. [Supplement] [Long version]
• Ryan Tibshirani. Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions. Neural Information Processing Systems, 2017. [Supplement]
• Veeranjaneyulu Sadhanala and Ryan Tibshirani. Additive Models with Trend Filtering. Annals of Statistics, Vol. 47, No. 6, 3032-3068, 2019.

2016

• Ryan Tibshirani and Saharon Rosset. Excess Optimism: How Biased is the Apparent Error of an Estimator Tuned by SURE? Journal of the American Statistical Association, Vol. 114, No. 526, 697-712, 2019.
• Oscar Hernan Madrid Padilla, James Sharpnack, James Scott, and Ryan Tibshirani. The DFS Fused Lasso: Linear-Time Denoising over General Graphs. Journal of Machine Learning Research, Vol. 18, No. 176, 1-36, 2018.
• Sangwon Hyun, Max G'Sell, and Ryan Tibshirani. Exact Post-Selection Inference for the Generalized Lasso Path. Electronic Journal of Statistics, Vol. 12, 1053-1097, 2018.
• Alnur Ali, Zico Kolter, and Ryan Tibshirani. The Multiple Quantile Graphical Model. Neural Information Processing Systems, 2016. [Supplement]
• Veeranjaneyulu Sadhanala, Yu-Xiang Wang, and Ryan Tibshirani. Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers. Neural Information Processing Systems, 2016. [Supplement]
• Jing Lei, Max G'Sell, Alessandro Rinaldo, Ryan Tibshirani, and Larry Wasserman. Distribution-Free Predictive Inference for Regression. Journal of the American Statistical Association, Vol. 113, No. 523, 1094-1111, 2018. [R package]
• David Farrow, Logan Brooks, Sangwon Hyun, Ryan Tibshirani, Donald Burke, and Roni Rosenfeld. A Human Judgment Approach to Epidemiological Forecasting. PLOS Computational Biology, Vol. 13, No. 3, 1-19, 2017. [Supplement]

2015

• William Fithian, Jonathan Taylor, Robert Tibshirani, and Ryan Tibshirani. Selective Sequential Model Selection. Technical report.
• Veeranjaneyulu Sadhanala, Yu-Xiang Wang, and Ryan Tibshirani. Graph Sparsification Approaches for Laplacian Smoothing. International Conference on Artificial Intelligence and Statistics, 2016. [Supplement]
• Ryan Tibshirani, Alessandro Rinaldo, Robert Tibshirani, and Larry Wasserman. Uniform Asymptotic Inference and the Bootstrap After Model Selection. Annals of Statistics, Vol. 46, No. 3, 1255-1287, 2018.
• Logan Brooks, David Farrow, Sangwon Hyun, Ryan Tibshirani, and Roni Rosenfeld. Flexible Modeling of Epidemics with an Empirical Bayes Framework. PLOS Computational Biology, Vol. 11, No. 8, 1-18, 2015. [Supplement]

2014

• Yen-Chi Chen, Christopher Genovese, Ryan Tibshirani, and Larry Wasserman. Nonparametric Modal Regression. Annals of Statistics, Vol. 44, No. 2, 489-514, 2016.
• Ryan Tibshirani. A General Framework for Fast Stagewise Algorithms. Journal of Machine Learning Research, Vol. 16, 2543-2588, 2015.
• Aaditya Ramdas and Ryan Tibshirani. Fast and Flexible ADMM Algorithms for Trend Filtering. Journal of Computational and Graphical Statistics, Vol. 25, No. 3, 839-858, 2016. [R package]
• Yu-Xiang Wang, James Sharpnack, Alex Smola, and Ryan Tibshirani. Trend Filtering on Graphs. Journal of Machine Learning Research, Vol. 17, 1-41, 2016. [Conference version]
• Taylor Arnold and Ryan Tibshirani. Efficient Implementations of the Generalized Lasso Dual Path Algorithm. Journal of Computational and Graphical Statistics, Vol. 25, No. 1, 1-27, 2016. [R package]
• Ryan Tibshirani. Degrees of Freedom and Model Search. Statistica Sinica, Vol. 25, No. 3, 1265-1296, 2015.
• Yu-Xiang Wang, Alex Smola, and Ryan Tibshirani. The Falling Factorial Basis and Its Statistical Applications. International Conference on Machine Learning, 2014. [Supplement]
• Ryan Tibshirani, Jonathan Taylor, Richard, Lockhart, and Robert Tibshirani. Exact Post-Selection Inference for Sequential Regression Procedures. Journal of the American Statistical Association, Vol. 111, No. 514, 600-620, 2016.

2013

• Jonathan Taylor, Joshua Loftus, and Ryan Tibshirani. Inference in Adaptive Regression via the Kac-Rice Formula. Annals of Statistics, Vol. 44, No. 2, 743-770, 2016.
• Ryan Tibshirani. Adaptive Piecewise Polynomial Estimation via Trend Filtering. Annals of Statistics, Vol. 42, No. 1, 285-323, 2014.
• Richard Lockhart, Jonathan Taylor, Ryan Tibshirani, and Robert Tibshirani. A Significance Test for the Lasso. Annals of Statistics, Vol. 4, No. 2, 413-468, 2014.
  • Discussion: Buhlmann, Meier, and van de Geer
  • Discussion: Cai and Yuan
  • Discussion: Fan and Ke
  • Discussion: Lv and Zheng
  • Discussion: Wasserman
  • Discussion: Buja and Brown
  • Rejoinder: Lockhart, Taylor, Tibshirani, and Tibshirani

2012

• Ryan Tibshirani. The Lasso Problem and Uniqueness. Electronic Journal of Statistics, Vol. 7, 1456-1490, 2013.

2011

• Ryan Tibshirani and Jonathan Taylor. Degrees of Freedom in Lasso Problems. Annals of Statistics, Vol. 40, No. 2, 1198-1232, 2012.
• Robert Tibshirani, Jacob Bien, Jerome Friedman, Trevor Hastie, Noah Simon, Jonathan Taylor, and Ryan Tibshirani. Strong Rules for Discarding Predictors in Lasso-Type Problems. Journal of the Royal Statistical Society: Series B, Vol. 74, No. 2, 245-266, 2012.

2010

• Ryan Tibshirani, Holger Hoefling, and Robert Tibshirani. Nearly-Isotonic Regression. Technometrics, Vol. 53, No. 1, 54-61, 2011.
• Ryan Tibshirani and Jonathan Taylor. The Solution Path of the Generalized Lasso. Annals of Statistics, Vol. 39, No. 3, 1335-1371, 2011. [Supplement] [R package]

2009

• Ryan Tibshirani, Andrew Price, and Jonathan Taylor. A Statistician Plays Darts. Journal of the Royal Statistical Society: Series A, Vol. 174, No. 1, 213-226, 2011. [Supplement] [R package] [Significance article] [Website]

2008

• Ryan Tibshirani and Robert Tibshirani. A Bias Correction for the Minimum Error Rate in Cross-Validation. Annals of Applied Statistics, Vol. 3, No. 1, 822-829, 2009.
• Ryan Tibshirani. Fast Computation of the Median by Successive Binning. Technical report. [Website]