Conformal prediction: A gentle introduction AN Angelopoulos, S Bates Foundations and Trends® in Machine Learning 16 (4), 494-591, 2023 | 540* | 2023 |
Uncertainty sets for image classifiers using conformal prediction A Angelopoulos*, S Bates*, J Malik, MI Jordan International Conference on Learning Representations, 2021 | 240 | 2021 |
Cross-validation: what does it estimate and how well does it do it? S Bates, T Hastie, R Tibshirani Journal of the American Statistical Association, 1-22, 2023 | 201 | 2023 |
Distribution-free, risk-controlling prediction sets S Bates*, A Angelopoulos*, L Lei*, J Malik, MI Jordan Journal of the ACM 68 (6), 2021 | 179 | 2021 |
Testing for outliers with conformal p-values S Bates, E Candès, L Lei, Y Romano, M Sesia Annals of Statistics 51 (1), 149-178, 2023 | 113 | 2023 |
Learn then test: Calibrating predictive algorithms to achieve risk control AN Angelopoulos, S Bates, EJ Candès, MI Jordan, L Lei arXiv preprint arXiv:2110.01052, 2021 | 98 | 2021 |
Multi-resolution localization of causal variants across the genome M Sesia, E Katsevich, S Bates, E Candès, C Sabatti Nature communications 11 (1), 1093, 2020 | 86 | 2020 |
Conformal risk control AN Angelopoulos, S Bates, A Fisch, L Lei, T Schuster International Conference on Learning Representations, 2024 | 78 | 2024 |
False discovery rate control in genome-wide association studies with population structure M Sesia, S Bates, E Candès, J Marchini, C Sabatti Proceedings of the National Academy of Sciences 118 (40), e2105841118, 2021 | 78* | 2021 |
Image-to-image regression with distribution-free uncertainty quantification and applications in imaging AN Angelopoulos, AP Kohli, S Bates, M Jordan, J Malik, T Alshaabi, ... International Conference on Machine Learning, 717-730, 2022 | 73 | 2022 |
Metropolized knockoff sampling S Bates, E Candès, L Janson, W Wang Journal of the American Statistical Association 116 (535), 1413-1427, 2021 | 73 | 2021 |
Causal inference in genetic trio studies S Bates, M Sesia, C Sabatti, E Candès Proceedings of the National Academy of Sciences 117 (39), 24117-24126, 2020 | 66 | 2020 |
Prediction-powered inference AN Angelopoulos, S Bates, C Fannjiang, MI Jordan, T Zrnic Science 382 (6671), 669-674, 2023 | 63 | 2023 |
Conformal prediction under feedback covariate shift for biomolecular design C Fannjiang, S Bates, AN Angelopoulos, J Listgarten, MI Jordan Proceedings of the National Academy of Sciences 119 (43), e2204569119, 2022 | 58 | 2022 |
Achieving Equalized Odds by Resampling Sensitive Attributes Y Romano, S Bates, EJ Candès Advances in Neural Information Processing Systems (NeurIPS), 2020 | 40 | 2020 |
Log-ratio lasso: scalable, sparse estimation for log-ratio models S Bates, R Tibshirani Biometrics 75 (2), 613-624, 2019 | 36 | 2019 |
The sample complexity of online contract design B Zhu, S Bates, Z Yang, Y Wang, J Jiao, MI Jordan Proceedings of the 24th ACM Conference on Economics and Computation, 2023 | 34 | 2023 |
Improving conditional coverage via orthogonal quantile regression S Feldman, S Bates, Y Romano Advances in neural information processing systems 34, 2060-2071, 2021 | 31 | 2021 |
Robust Calibration with Multi-domain Temperature Scaling Y Yu, S Bates, Y Ma, MI Jordan Advances in Neural Information Processing Systems (NeurIPS), 2022 | 30 | 2022 |
Achieving risk control in online learning settings S Feldman, L Ringel, S Bates, Y Romano Transactions on Machine Learning Research, 2023 | 24* | 2023 |