Tibshirani, Ryan J.

20 publications

JMLR 2025 Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test Seunghoon Paik, Michael Celentano, Alden Green, Ryan J. Tibshirani

JMLR 2025 Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method Ryan J. Tibshirani, Samy Wu Fung, Howard Heaton, Stanley Osher

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

NeurIPS 2023 Conformal PID Control for Time Series Prediction Anastasios Angelopoulos, Emmanuel Candes, Ryan J Tibshirani

JMLR 2023 Flexible Model Aggregation for Quantile Regression Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani

FnTML 2022 Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems Ryan J. Tibshirani

JMLR 2021 Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani

AISTATS 2019 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, Aaditya Ramdas, Ryan J. Tibshirani

NeurIPS 2019 Conformal Prediction Under Covariate Shift Ryan J Tibshirani, Rina Foygel Barber, Emmanuel Candes, Aaditya Ramdas

NeurIPS 2019 Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights Maria Jahja, David Farrow, Roni Rosenfeld, Ryan J Tibshirani

NeurIPS 2017 A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening Kevin Lin, James L Sharpnack, Alessandro Rinaldo, Ryan J Tibshirani

NeurIPS 2017 Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions Ryan J Tibshirani

NeurIPS 2017 Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James L Sharpnack, Ryan J Tibshirani

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

NeurIPS 2016 The Multiple Quantile Graphical Model Alnur Ali, J. Zico Kolter, Ryan J Tibshirani

NeurIPS 2016 Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J Tibshirani

JMLR 2016 Trend Filtering on Graphs Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani

JMLR 2015 A General Framework for Fast Stagewise Algorithms Ryan J. Tibshirani

AISTATS 2015 Trend Filtering on Graphs Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani