| 2026 | COLT | Sample-Efficient Omniprediction for Proper Losses. | Isaac Gibbs, Ryan J. Tibshirani |
| 2026 | COLT | Blackwell Approachability and Gradient Equilibrium are Equivalent. | Brian W. Lee, Nika Haghtalab, Michael I. Jordan, Ryan J. Tibshirani |
| 2024 | AISTATS | Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent. | Pratik Patil, Yuchen Wu, Ryan J. Tibshirani |
| 2024 | ICML | Optimal Ridge Regularization for Out-of-Distribution Prediction. | Pratik Patil, Jin-Hong Du, Ryan J. Tibshirani |
| 2022 | AISTATS | Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression. | Pratik Patil, Alessandro Rinaldo, Ryan J. Tibshirani |
| 2021 | AISTATS | Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs. | Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani |
| 2021 | AISTATS | Uniform Consistency of Cross-Validation Estimators for High-Dimensional Ridge Regression. | Pratik Patil, Yuting Wei, Alessandro Rinaldo, Ryan J. Tibshirani |
| 2020 | ICML | The Implicit Regularization of Stochastic Gradient Flow for Least Squares. | Alnur Ali, Edgar Dobriban, Ryan J. Tibshirani |
| 2019 | AISTATS | A Continuous-Time View of Early Stopping for Least Squares Regression. | Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani |
| 2019 | AISTATS | A Higher-Order Kolmogorov-Smirnov Test. | Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani |
| 2016 | AISTATS | Graph Sparsification Approaches for Laplacian Smoothing. | Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani |
| 2015 | AISTATS | Trend Filtering on Graphs. | Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani |
| 2014 | ICML | The Falling Factorial Basis and Its Statistical Applications. | Yu-Xiang Wang, Alexander J. Smola, Ryan J. Tibshirani |