Ryan J. Tibshirani

61 papers A* 7A 8Journal 41Unranked 5

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Basic Inequalities for First-Order Optimization with Applications to Statistical Risk Analysis. CoRR Seunghoon Paik, Kangjie Zhou, Matus Telgarsky, Ryan J. Tibshirani
2025	J	`jnl`	Calibrated Multi-Level Quantile Forecasting. CoRR Tiffany Ding, Isaac Gibbs, Ryan J. Tibshirani
2025	J	`jnl`	Gradient Equilibrium in Online Learning: Theory and Applications. CoRR Anastasios N. Angelopoulos, Michael I. Jordan, Ryan J. Tibshirani
2025	J	`jnl`	Gradient Equilibrium in Online Learning: Theory and Applications. J. Mach. Learn. Res. Anastasios N. Angelopoulos, Michael I. Jordan, Ryan J. Tibshirani
2025	J	`jnl`	Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test. J. Mach. Learn. Res. Seunghoon Paik, Michael Celentano, Alden Green, Ryan J. Tibshirani
2025	J	`jnl`	Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method. J. Mach. Learn. Res. Ryan J. Tibshirani, Samy Wu Fung, Howard Heaton, Stanley J. Osher
2025	J	`jnl`	Nowcasting reported covid-19 hospitalizations using de-identified, aggregated medical insurance claims data. PLoS Comput. Biol. Xueda Shen, Aaron Rumack, Bryan Wilder, Ryan J. Tibshirani
2025	J	`jnl`	Sample-Efficient Omniprediction for Proper Losses. CoRR Isaac Gibbs, Ryan J. Tibshirani
2024	J	`jnl`	Asymptotics of the Sketched Pseudoinverse. SIAM J. Math. Data Sci. Daniel LeJeune, Pratik Patil, Hamid Javadi, Richard G. Baraniuk, Ryan J. Tibshirani
2024	A	`conf`	Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent. AISTATS Pratik Patil, Yuchen Wu, Ryan J. Tibshirani
2024	J	`jnl`	Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent. CoRR Pratik Patil, Yuchen Wu, Ryan J. Tibshirani
2024	A*	`conf`	Optimal Ridge Regularization for Out-of-Distribution Prediction. ICML Pratik Patil, Jin-Hong Du, Ryan J. Tibshirani
2024	J	`jnl`	Optimal Ridge Regularization for Out-of-Distribution Prediction. CoRR Pratik Patil, Jin-Hong Du, Ryan J. Tibshirani
2024	J	`jnl`	Revisiting Optimism and Model Complexity in the Wake of Overparameterized Machine Learning. CoRR Pratik Patil, Jin-Hong Du, Ryan J. Tibshirani
2023	J	`jnl`	Class-Conditional Conformal Prediction With Many Classes. CoRR Tiffany Ding, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani
2023	A*	`conf`	Class-Conditional Conformal Prediction with Many Classes. NeurIPS Tiffany Ding, Anastasios Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani
2023	A*	`conf`	Conformal PID Control for Time Series Prediction. NeurIPS Anastasios Angelopoulos, Emmanuel J. Candès, Ryan J. Tibshirani
2023	J	`jnl`	Conformal PID Control for Time Series Prediction. CoRR Anastasios N. Angelopoulos, Emmanuel J. Candès, Ryan J. Tibshirani
2023	J	`jnl`	Flexible Model Aggregation for Quantile Regression. J. Mach. Learn. Res. Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani
2023	J	`jnl`	Maximum Mean Discrepancy Meets Neural Networks: The Radon-Kolmogorov-Smirnov Test. CoRR Seunghoon Paik, Michael Celentano, Alden Green, Ryan J. Tibshirani
2022	J	`jnl`	Asymptotics of the Sketched Pseudoinverse. CoRR Daniel LeJeune, Pratik Patil, Hamid Javadi, Richard G. Baraniuk, Ryan J. Tibshirani
2022	J	`jnl`	Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems. Found. Trends Mach. Learn. Ryan J. Tibshirani
2022	A	`conf`	Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression. AISTATS Pratik Patil, Alessandro Rinaldo, Ryan J. Tibshirani
2022	J	`jnl`	Recalibrating probabilistic forecasts of epidemics. PLoS Comput. Biol. Aaron Rumack, Ryan J. Tibshirani, Roni Rosenfeld
2022	J	`jnl`	The Voronoigram: Minimax Estimation of Bounded Variation Functions From Scattered Data. CoRR Addison J. Hu, Alden Green, Ryan J. Tibshirani
2021	J	`jnl`	Deep Quantile Aggregation. CoRR Taesup Kim, Rasool Fakoor, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani
2021	J	`jnl`	Epidemic tracking and forecasting: Lessons learned from a tumultuous year. Proc. Natl. Acad. Sci. USA Roni Rosenfeld, Ryan J. Tibshirani
2021	A	`conf`	Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs. AISTATS Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
2021	J	`jnl`	Multivariate Trend Filtering for Lattice Data. CoRR Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Addison J. Hu, Ryan J. Tibshirani
2021	J	`jnl`	Recalibrating probabilistic forecasts of epidemics. CoRR Aaron Rumack, Ryan J. Tibshirani, Roni Rosenfeld
2021	J	`jnl`	Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs. J. Mach. Learn. Res. Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
2021	A	`conf`	Uniform Consistency of Cross-Validation Estimators for High-Dimensional Ridge Regression. AISTATS Pratik Patil, Yuting Wei, Alessandro Rinaldo, Ryan J. Tibshirani
2020	J	`jnl`	Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems. CoRR Ryan J. Tibshirani
2020	A*	`conf`	The Implicit Regularization of Stochastic Gradient Flow for Least Squares. ICML Alnur Ali, Edgar Dobriban, Ryan J. Tibshirani
2020	J	`jnl`	The Implicit Regularization of Stochastic Gradient Flow for Least Squares. CoRR Alnur Ali, Edgar Dobriban, Ryan J. Tibshirani
2019	A	`conf`	A Continuous-Time View of Early Stopping for Least Squares Regression. AISTATS Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani
2019	A	`conf`	A Higher-Order Kolmogorov-Smirnov Test. AISTATS Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani
2019	J	`jnl`	A Higher-Order Kolmogorov-Smirnov Test. CoRR Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani
2019	A*	`conf`	Conformal Prediction Under Covariate Shift. NeurIPS Ryan J. Tibshirani, Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas
2019	A*	`conf`	Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights. NeurIPS Maria Jahja, David C. Farrow, Roni Rosenfeld, Ryan J. Tibshirani
2019	J	`jnl`	Surprises in High-Dimensional Ridgeless Least Squares Interpolation. CoRR Trevor Hastie, Andrea Montanari, Saharon Rosset, Ryan J. Tibshirani
2018	J	`jnl`	A Continuous-Time View of Early Stopping for Least Squares Regression. CoRR Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani
2018	J	`jnl`	Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions. PLoS Comput. Biol. Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani, Roni Rosenfeld
2017	—	`conf`	A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening. NIPS Kevin Lin, James Sharpnack, Alessandro Rinaldo, Ryan J. Tibshirani
2017	J	`jnl`	A human judgment approach to epidemiological forecasting. PLoS Comput. Biol. David C. Farrow, Logan C. Brooks, Sangwon Hyun, Ryan J. Tibshirani, Donald S. Burke, Roni Rosenfeld
2017	—	`conf`	Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions. NIPS Ryan J. Tibshirani
2017	—	`conf`	Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods. NIPS Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James Sharpnack, Ryan J. Tibshirani
2017	J	`jnl`	The DFS Fused Lasso: Linear-Time Denoising over General Graphs. J. Mach. Learn. Res. Oscar Hernan Madrid Padilla, James Sharpnack, James G. Scott, Ryan J. Tibshirani
2016	A	`conf`	Graph Sparsification Approaches for Laplacian Smoothing. AISTATS Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani
2016	—	`conf`	The Multiple Quantile Graphical Model. NIPS Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani
2016	—	`conf`	Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers. NIPS Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani
2016	J	`jnl`	Trend Filtering on Graphs. J. Mach. Learn. Res. Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani
2015	J	`jnl`	A general framework for fast stagewise algorithms. J. Mach. Learn. Res. Ryan J. Tibshirani
2015	J	`jnl`	Flexible Modeling of Epidemics with an Empirical Bayes Framework. PLoS Comput. Biol. Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani, Roni Rosenfeld
2015	A	`conf`	Trend Filtering on Graphs. AISTATS Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani
2014	J	`jnl`	Efficient Implementations of the Generalized Lasso Dual Path Algorithm. CoRR Taylor B. Arnold, Ryan J. Tibshirani
2014	J	`jnl`	Fast and Flexible ADMM Algorithms for Trend Filtering. CoRR Aaditya Ramdas, Ryan J. Tibshirani
2014	A*	`conf`	The Falling Factorial Basis and Its Statistical Applications. ICML Yu-Xiang Wang, Alexander J. Smola, Ryan J. Tibshirani
2014	J	`jnl`	Trend Filtering on Graphs. CoRR Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani
2011	J	`jnl`	Nearly-Isotonic Regression. Technometrics Ryan J. Tibshirani, Holger Höfling, Robert Tibshirani
2008	J	`jnl`	Fast computation of the median by successive binning CoRR Ryan J. Tibshirani