Ryan Tibshirani

Research groups


StatML (Statistics & Machine Learning) Reading Group	DELPHI, Epidemiological Forecasting Group

Research papers

(The headings refer to the completion dates; the publication dates are given at the end of the citations.)

2020

Ryan Tibshirani. Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems.

2019

Guo Yu, Jacob Bien, and Ryan Tibshirani. Reluctant Interaction Modeling.
Alden Green, Sivaraman Balakrishnan, and Ryan Tibshirani. Local Spectral Clustering Recovers Density Clusters.
David Farrow, Maria Jahja, Roni Rosenfeld, and Ryan Tibshirani. Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights. Neural Information Processing Systems.
- Supplement: Proofs and Additional Experiments
Rina Foygel Barber, Emmanuel Candes, Aaditya Ramdas, and Ryan Tibshirani. Conformal Prediction Under Covariate Shift. Neural Information Processing Systems.
- Supplement: Proofs and Additional Experiments
Trevor Hastie, Andrea Montanari, Saharon Rosset, and Ryan Tibshirani. Surprises in High-Dimensional Ridgeless Least Squares Interpolation.
Rina Foygel Barber, Emmanuel Candes, Aaditya Ramdas, and Ryan Tibshirani. The Limits of Distribution-Free Conditional Predictive Inference. To appear, Information and Inference.
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: Proofs and Additional Experiments
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: Proofs and Additional Experiments
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: Additional Figures and Explanations
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. To appear, Biometrics.
- Supplement: Additional Details and Experiments

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: Full Set of Simulation Results
- R package: "bestsubset" (also includes code for reproducing all simulations and figures)
- 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: Proofs and Additional Details
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: Proofs and Experiments.
- Earlier version: Approximate Recovery in Changepoint Problems, from ℓ2 Estimation Error Rates.
Ryan Tibshirani. Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions. Neural Information Processing Systems, 2017.
- Supplement: Proofs and Additional Details.
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: Proofs and Additional Experiments
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: Proofs and Additional Experiments
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: "conformalInference" (also includes code for reproducing all simulations and figures)
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: Additional Figures and Explanations

2015

William Fithian, Jonathan Taylor, Robert Tibshirani, and Ryan Tibshirani. Selective Sequential Model Selection.
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, and Ryan Tibshirani. Graph Sparsification Approaches for Laplacian Smoothing. International Conference on Artificial Intelligence and Statistics, 2016.
- Supplement: Proofs and Additional Experiments
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: Additional Figures and Explanations

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: "glmgen" (also available as standalone C package, same link)
Yu-Xiang Wang, James Sharpnack, Alex Smola, and Ryan Tibshirani. Trend Filtering on Graphs. Journal of Machine Learning Research, Vol. 17, 1-41, 2016.
- Short version: Trend Filtering on Graphs, International Conference on Artificial Intelligence and Statistics, 2015.
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: "genlasso"
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: Proofs and Additional Experiments
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. 42, No. 2, 413-468, 2014.

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: Proofs and Technical Details
- R package: "genlasso"

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.

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.
- Median webpage