Ryan J. Tibshirani

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 42

Top Topics

Kolmogorov Smirnov

Ridge Regression

Top Venues

J. Mach. Learn. Res.

Publications

Daniel LeJeune, Pratik Patil, Hamid Javadi, Richard G. Baraniuk, Ryan J. Tibshirani
Asymptotics of the Sketched Pseudoinverse. SIAM J. Math. Data Sci. 6 (1) (2024)
Pratik Patil, Jin-Hong Du, Ryan J. Tibshirani
Optimal Ridge Regularization for Out-of-Distribution Prediction. CoRR (2024)
Pratik Patil, Yuchen Wu, Ryan J. Tibshirani
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent. AISTATS (2024)
Pratik Patil, Yuchen Wu, Ryan J. Tibshirani
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent. CoRR (2024)
Seunghoon Paik, Michael Celentano, Alden Green, Ryan J. Tibshirani
Maximum Mean Discrepancy Meets Neural Networks: The Radon-Kolmogorov-Smirnov Test. CoRR (2023)
Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani
Flexible Model Aggregation for Quantile Regression. J. Mach. Learn. Res. 24 (2023)
Tiffany Ding, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani
Class-Conditional Conformal Prediction With Many Classes. CoRR (2023)
Tiffany Ding, Anastasios Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani
Class-Conditional Conformal Prediction with Many Classes. NeurIPS (2023)
Anastasios Angelopoulos, Emmanuel J. Candès, Ryan J. Tibshirani
Conformal PID Control for Time Series Prediction. NeurIPS (2023)
Anastasios N. Angelopoulos, Emmanuel J. Candès, Ryan J. Tibshirani
Conformal PID Control for Time Series Prediction. CoRR (2023)
Daniel LeJeune, Pratik Patil, Hamid Javadi, Richard G. Baraniuk, Ryan J. Tibshirani
Asymptotics of the Sketched Pseudoinverse. CoRR (2022)
Aaron Rumack, Ryan J. Tibshirani, Roni Rosenfeld
Recalibrating probabilistic forecasts of epidemics. PLoS Comput. Biol. 18 (12) (2022)
Ryan J. Tibshirani
Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems. Found. Trends Mach. Learn. 15 (6) (2022)
Pratik Patil, Alessandro Rinaldo, Ryan J. Tibshirani
Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression. AISTATS (2022)
Addison J. Hu, Alden Green, Ryan J. Tibshirani
The Voronoigram: Minimax Estimation of Bounded Variation Functions From Scattered Data. CoRR (2022)
Aaron Rumack, Ryan J. Tibshirani, Roni Rosenfeld
Recalibrating probabilistic forecasts of epidemics. CoRR (2021)
Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs. J. Mach. Learn. Res. 22 (2021)
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Addison J. Hu, Ryan J. Tibshirani
Multivariate Trend Filtering for Lattice Data. CoRR (2021)
Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs. AISTATS (2021)
Pratik Patil, Yuting Wei, Alessandro Rinaldo, Ryan J. Tibshirani
Uniform Consistency of Cross-Validation Estimators for High-Dimensional Ridge Regression. AISTATS (2021)
Roni Rosenfeld, Ryan J. Tibshirani
Epidemic tracking and forecasting: Lessons learned from a tumultuous year. Proc. Natl. Acad. Sci. USA 118 (51) (2021)
Taesup Kim, Rasool Fakoor, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani
Deep Quantile Aggregation. CoRR (2021)
Alnur Ali, Edgar Dobriban, Ryan J. Tibshirani
The Implicit Regularization of Stochastic Gradient Flow for Least Squares. CoRR (2020)
Ryan J. Tibshirani
Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems. CoRR (2020)
Alnur Ali, Edgar Dobriban, Ryan J. Tibshirani
The Implicit Regularization of Stochastic Gradient Flow for Least Squares. ICML (2020)
Maria Jahja, David C. Farrow, Roni Rosenfeld, Ryan J. Tibshirani
Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights. NeurIPS (2019)
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani
A Higher-Order Kolmogorov-Smirnov Test. CoRR (2019)
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani
A Higher-Order Kolmogorov-Smirnov Test. AISTATS (2019)
Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani
A Continuous-Time View of Early Stopping for Least Squares Regression. AISTATS (2019)
Trevor Hastie, Andrea Montanari, Saharon Rosset, Ryan J. Tibshirani
Surprises in High-Dimensional Ridgeless Least Squares Interpolation. CoRR (2019)
Ryan J. Tibshirani, Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas
Conformal Prediction Under Covariate Shift. NeurIPS (2019)
Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani, Roni Rosenfeld
Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions. PLoS Comput. Biol. 14 (6) (2018)
Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani
A Continuous-Time View of Early Stopping for Least Squares Regression. CoRR (2018)
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James Sharpnack, Ryan J. Tibshirani
Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods. NIPS (2017)
Ryan J. Tibshirani
Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions. NIPS (2017)
Oscar Hernan Madrid Padilla, James Sharpnack, James G. Scott, Ryan J. Tibshirani
The DFS Fused Lasso: Linear-Time Denoising over General Graphs. J. Mach. Learn. Res. 18 (2017)
Kevin Lin, James Sharpnack, Alessandro Rinaldo, Ryan J. Tibshirani
A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening. NIPS (2017)
David C. Farrow, Logan C. Brooks, Sangwon Hyun, Ryan J. Tibshirani, Donald S. Burke, Roni Rosenfeld
A human judgment approach to epidemiological forecasting. PLoS Comput. Biol. 13 (3) (2017)
Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani
The Multiple Quantile Graphical Model. NIPS (2016)
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani
Graph Sparsification Approaches for Laplacian Smoothing. AISTATS (2016)
Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani
Trend Filtering on Graphs. J. Mach. Learn. Res. 17 (2016)
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers. NIPS (2016)
Ryan J. Tibshirani
A general framework for fast stagewise algorithms. J. Mach. Learn. Res. 16 (2015)
Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani
Trend Filtering on Graphs. AISTATS (2015)
Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani, Roni Rosenfeld
Flexible Modeling of Epidemics with an Empirical Bayes Framework. PLoS Comput. Biol. 11 (8) (2015)
Taylor B. Arnold, Ryan J. Tibshirani
Efficient Implementations of the Generalized Lasso Dual Path Algorithm. CoRR (2014)
Aaditya Ramdas, Ryan J. Tibshirani
Fast and Flexible ADMM Algorithms for Trend Filtering. CoRR (2014)
Yu-Xiang Wang, Alexander J. Smola, Ryan J. Tibshirani
The Falling Factorial Basis and Its Statistical Applications. ICML (2014)
Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani
Trend Filtering on Graphs. CoRR (2014)
Ryan J. Tibshirani, Holger Höfling, Robert Tibshirani
Nearly-Isotonic Regression. Technometrics 53 (1) (2011)
Ryan J. Tibshirani
Fast computation of the median by successive binning CoRR (2008)