Fast computation of the median by successive binning - Ryan J. Tibshirani (CoRR, 2008)

Nearly-Isotonic Regression. - Ryan J. Tibshirani; Holger Höfling; Robert Tibshirani (Technometrics, 2011)

The Falling Factorial Basis and Its Statistical Applications. - Yu-Xiang Wang 003; Alexander J. Smola; Ryan J. Tibshirani (ICML, 2014)

Efficient Implementations of the Generalized Lasso Dual Path Algorithm. - Taylor B. Arnold; Ryan J. Tibshirani (CoRR, 2014)

Fast and Flexible ADMM Algorithms for Trend Filtering. - Aaditya Ramdas; Ryan J. Tibshirani (CoRR, 2014)

A general framework for fast stagewise algorithms. - Ryan J. Tibshirani (J. Mach. Learn. Res., 2015)

Flexible Modeling of Epidemics with an Empirical Bayes Framework. - Logan C. Brooks; David C. Farrow; Sangwon Hyun; Ryan J. Tibshirani; Roni Rosenfeld (PLoS Comput. Biol., 2015)

Graph Sparsification Approaches for Laplacian Smoothing. - Veeranjaneyulu Sadhanala; Yu-Xiang Wang 003; Ryan J. Tibshirani (AISTATS, 2016)

The Multiple Quantile Graphical Model. - Alnur Ali; J. Zico Kolter; Ryan J. Tibshirani (NIPS, 2016)

Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers. - Veeranjaneyulu Sadhanala; Yu-Xiang Wang 003; Ryan J. Tibshirani (NIPS, 2016)

Trend Filtering on Graphs. - Yu-Xiang Wang 003; James Sharpnack; Alexander J. Smola; Ryan J. Tibshirani (J. Mach. Learn. Res., 2016)

A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening. - Kevin Lin; James Sharpnack; Alessandro Rinaldo; Ryan J. Tibshirani (NIPS, 2017)

Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods. - Veeranjaneyulu Sadhanala; Yu-Xiang Wang 003; James Sharpnack; Ryan J. Tibshirani (NIPS, 2017)

Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions. - Ryan J. Tibshirani (NIPS, 2017)

The DFS Fused Lasso: Linear-Time Denoising over General Graphs. - Oscar Hernan Madrid Padilla; James Sharpnack; James G. Scott; Ryan J. Tibshirani (J. Mach. Learn. Res., 2017)

A human judgment approach to epidemiological forecasting. - David C. Farrow; Logan C. Brooks; Sangwon Hyun; Ryan J. Tibshirani; Donald S. Burke; Roni Rosenfeld (PLoS Comput. Biol., 2017)

Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions. - Logan C. Brooks; David C. Farrow; Sangwon Hyun; Ryan J. Tibshirani; Roni Rosenfeld (PLoS Comput. Biol., 2018)

A Continuous-Time View of Early Stopping for Least Squares Regression. - Alnur Ali; J. Zico Kolter; Ryan J. Tibshirani (AISTATS, 2019)

A Higher-Order Kolmogorov-Smirnov Test. - Veeranjaneyulu Sadhanala; Yu-Xiang Wang 003; Aaditya Ramdas; Ryan J. Tibshirani (AISTATS, 2019)

Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights. - Maria Jahja; David C. Farrow; Roni Rosenfeld; Ryan J. Tibshirani (NeurIPS, 2019)

Conformal Prediction Under Covariate Shift. - Ryan J. Tibshirani; Rina Foygel Barber; Emmanuel J. Candès; Aaditya Ramdas (NeurIPS, 2019)

Surprises in High-Dimensional Ridgeless Least Squares Interpolation. - Trevor Hastie; Andrea Montanari; Saharon Rosset; Ryan J. Tibshirani (CoRR, 2019)

The Implicit Regularization of Stochastic Gradient Flow for Least Squares. - Alnur Ali; Edgar Dobriban; Ryan J. Tibshirani (ICML, 2020)

Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs. - Alden Green; Sivaraman Balakrishnan; Ryan J. Tibshirani (AISTATS, 2021)

Uniform Consistency of Cross-Validation Estimators for High-Dimensional Ridge Regression. - Pratik Patil; Yuting Wei 001; Alessandro Rinaldo; Ryan J. Tibshirani (AISTATS, 2021)

Deep Quantile Aggregation. - Taesup Kim; Rasool Fakoor; Jonas Mueller 001; Alexander J. Smola; Ryan J. Tibshirani (CoRR, 2021)

Multivariate Trend Filtering for Lattice Data. - Veeranjaneyulu Sadhanala; Yu-Xiang Wang 003; Addison J. Hu; Ryan J. Tibshirani (CoRR, 2021)

Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs. - Alden Green; Sivaraman Balakrishnan; Ryan J. Tibshirani (J. Mach. Learn. Res., 2021)

Epidemic tracking and forecasting: Lessons learned from a tumultuous year. - Roni Rosenfeld; Ryan J. Tibshirani (Proc. Natl. Acad. Sci. USA, 2021)

Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression. - Pratik Patil; Alessandro Rinaldo; Ryan J. Tibshirani (AISTATS, 2022)

The Voronoigram: Minimax Estimation of Bounded Variation Functions From Scattered Data. - Addison J. Hu; Alden Green; Ryan J. Tibshirani (CoRR, 2022)

Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems. - Ryan J. Tibshirani (Found. Trends Mach. Learn., 2022)

Recalibrating probabilistic forecasts of epidemics. - Aaron Rumack; Ryan J. Tibshirani; Roni Rosenfeld (PLoS Comput. Biol., 2022)

Conformal PID Control for Time Series Prediction. - Anastasios Angelopoulos; Emmanuel J. Candès; Ryan J. Tibshirani (NeurIPS, 2023)

Class-Conditional Conformal Prediction with Many Classes. - Tiffany Ding; Anastasios Angelopoulos; Stephen Bates; Michael I. Jordan; Ryan J. Tibshirani (NeurIPS, 2023)

Maximum Mean Discrepancy Meets Neural Networks: The Radon-Kolmogorov-Smirnov Test. - Seunghoon Paik; Michael Celentano; Alden Green; Ryan J. Tibshirani (CoRR, 2023)

Flexible Model Aggregation for Quantile Regression. - Rasool Fakoor; Taesup Kim; Jonas Mueller 001; Alexander J. Smola; Ryan J. Tibshirani (J. Mach. Learn. Res., 2023)

Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent. - Pratik Patil; Yuchen Wu; Ryan J. Tibshirani (AISTATS, 2024)

Optimal Ridge Regularization for Out-of-Distribution Prediction. - Pratik Patil; Jin-Hong Du; Ryan J. Tibshirani (ICML, 2024)

Revisiting Optimism and Model Complexity in the Wake of Overparameterized Machine Learning. - Pratik Patil; Jin-Hong Du; Ryan J. Tibshirani (CoRR, 2024)

Asymptotics of the Sketched Pseudoinverse. - Daniel LeJeune; Pratik Patil; Hamid Javadi; Richard G. Baraniuk; Ryan J. Tibshirani (SIAM J. Math. Data Sci., 2024)

Nowcasting reported covid-19 hospitalizations using de-identified, aggregated medical insurance claims data. - Xueda Shen; Aaron Rumack; Bryan Wilder; Ryan J. Tibshirani (PLoS Comput. Biol., 2025)

Gradient Equilibrium in Online Learning: Theory and Applications. - Anastasios N. Angelopoulos; Michael I. Jordan; Ryan J. Tibshirani (CoRR, 2025)