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Publications and preprints
- “Conformal Triage for Medical Imaging AI Deployment”
A. N. Angelopoulos, S. Pomerantz, S. Do, S. Bates, C. P. Bridge, D. C. Elton, M. H. Lev, R. G. González, M. I. Jordan, J. Malik, medRxiv preprint, 2024.
[medRxiv]
[code]
[bibtex]
- “Online conformal prediction with decaying step sizes”
A. N. Angelopoulos, R. F. Barber, S. Bates, arXiv preprint, 2024.
[arXiv]
[code]
[bibtex]
- “Calibrated Identification of Feature Dependencies in Single-cell Multiomics”
P. Boyeau, S. Bates, C. Ergen, M.I. Jordan, N. Yosef. bioRxiv preprint, 2023.
[bioRxiv]
[code]
[bibtex]
- “Delegating Data Collection in Decentralized Machine Learning”
N. Ananthakrishnan, S. Bates, M. I. Jordan, N. Haghtalab. arXiv preprint, 2023.
[arXiv]
[bibtex]
- “Class-Conditional Conformal Prediction With Many Classes”
T. Ding, A. Angelopoulos, S. Bates, M. I. Jordan, R. J. Tibshirani. NeurIPS, 2023.
[arXiv]
[bibtex]
- “Uncertainty Intervals for Prediction Errors in Time Series Forecasting”
H. Xu, S. Mei, S. Bates, J. Taylor, R. Tibshirani. arXiv preprint, 2023.
[arXiv]
[bibtex]
- “Incentive-Theoretic Bayesian Inference for Collaborative Science”
S. Bates, M. I. Jordan, M.Sklar, J. A. Soloff. arXiv preprint, 2023.
[arXiv]
[bibtex]
- “Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry”
S. Wang, S. Bates, P. M. Aronow, M. I. Jordan.
AISTATS, 2024. (oral presentation)
[arXiv]
[bibtex]
- “Prediction-Powered Inference”
A. N. Angelopoulos, S. Bates, C. Fannjiang, M. I. Jordan, and T. Zrnic. Science, 2023.
[arXiv]
[free journal version]
[journal]
[code]
[bibtex]
- “The Sample Complexity of Online Contract Design”
B. Zhu, S. Bates, Z. Yang, Y. Wang, J. Jiao, M. I. Jordan.
ACM Conference on Economics and Computation, 2023.
[arXiv]
[journal]
[bibtex]
- “Conformal Prediction is Robust to Label Noise”
B. Einbinder, S. Bates, A. N. Angelopoulos, A. Gendler, and Y. Romano. arXiv preprint, 2022.
[arXiv]
[bibtex]
- “Conformal Risk Control”
A. N. Angelopoulos, S. Bates, A. Fisch, L. Lei, T. Schuster.
ICLR, 2024. (spotlight presentation)
[arXiv]
[code]
[bibtex]
- “Achieving Risk Control in Online Learning Settings”
S. Feldman, L. Ringel, S. Bates, and Y. Romano.
Transactions on Machine Learning Research, 2023.
[arXiv]
[journal]
[code]
[bibtex]
- “Semantic uncertainty intervals for disentangled latent spaces”
S. Sankaranarayanan, A. N. Angelopoulos, S. Bates, Y. Romano, P. Isola. NeurIPS, 2022.
[arXiv]
[homepage]
[code]
[bibtex]
- “Recommendation Systems with Distribution-Free Reliability Guarantees”
A. N. Angelopoulos, K. Krauth, S. Bates, Y. Wang, M. I. Jordan.
Symposium on Conformal and Probabilistic Prediction with Applications (COPA), 2023.
[arXiv]
[bibtex]
*Alexey Chervonenkis Best Paper Award
- “Robust Calibration with Multi-domain Temperature Scaling”
Y. Yu, S. Bates, Y. Ma, M. I. Jordan. NeurIPS, 2022.
[arXiv]
[code]
[bibtex]
- “Principal-Agent Hypothesis Testing”
S. Bates, M. I. Jordan, M. Sklar, and J. A. Soloff. arXiv preprint, 2022.
[arXiv]
[bibtex]
- “Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging”
A. N. Angelopoulos, A. P. Kohli, S. Bates, J. Malik, M. I. Jordan,
T. Alshaabi, S. Upadhyahyula, and Y. Romano. ICML, 2022.
[arXiv]
[code]
[bibtex]
- “Conformal Prediction Under Feedback Covariate Shift for Biomolecular Design”
C. Fannjiang, S. Bates, A. Angelopoulos, J. Listgarten, and M. I. Jordan.
Proceedings of the National Academy of Sciences of the USA (PNAS) 2022.
[arXiv]
[code]
[bibtex]
- “Causal Inference with Orthogonalized Regression Adjustment: Taming the Phantom”
S. Bates, E. Kennedy, R. Tibshirani, V. Ventura, and L. Wasserman.
Statistical Science, major revision, 2023+.
[arXiv]
[code]
[bibtex]
- “Confidence Intervals for the Generalisation Error of Random Forests”
S. Rajanala, S. Bates, T. Hastie, and R. Tibshirani. arXiv preprint, 2022.
[arXiv]
[code]
[bibtex]
- “Online Active Learning with Dynamic Marginal Gain Thresholding”
M. Werner, A. Angelopoulos, S. Bates, and M. I. Jordan. arXiv preprint, 2022.
[arXiv]
[bibtex]
- “Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control”
A. Angelopoulos, S. Bates, E. Candès, M. I. Jordan, and L. Lei. arXiv preprint, 2021.
[arXiv]
[video]
[code]
[bibtex]
- “Calibrated Multiple-Output Quantile Regression with Representation Learning”
S. Feldman, S. Bates, and Y. Romano. Journal of Machine Learning Research, 2023.
[arXiv]
[journal]
[code]
[bibtex]
- “Conformal Prediction: A Gentle Introduction”
A. Angelopoulos and S. Bates. Foundations and Trends in Machine Learning, 2023.
[arXiv]
[journal]
[video]
[bibtex]
- “Test-time Collective Prediction”
C. Mendler-Dünner, W. Guo, S. Bates, and M. I. Jordan. NeurIPS, 2021.
[arXiv]
[bibtex]
- “Improving Conditional Coverage via Orthogonal Quantile Regression”
S. Feldman, S. Bates, and Y. Romano. NeurIPS, 2021.
[arXiv]
[code]
[bibtex]
- “Testing for Outliers with Conformal p-values”
S. Bates, E. Candès, L. Lei, Y. Romano, and M. Sesia. Annals of Statistics, 2023.
[arXiv]
[journal]
[code]
[bibtex]
- “Cross-validation: what does it estimate and how well does it do it?”
S. Bates, T. Hastie, and R. Tibshirani.
Journal of the American Statistical Association (JASA), 2023.
[arXiv]
[journal]
[code]
[bibtex]
- “Private Prediction Sets”
A. Angelopoulos, S. Bates, T. Zrnic, and M. I. Jordan. Harvard Data Science Review, 2022.
[arXiv]
[journal]
[code]
[bibtex]
- “Distribution-Free, Risk-Controlling Prediction Sets”
S. Bates, A. Angelopoulos, L. Lei, J. Malik, and M. I. Jordan. Journal of the ACM, 2021.
[arXiv]
[journal]
[video]
[blog]
[code]
[bibtex]
- “Uncertainty Sets for Image Classifiers using Conformal Prediction”
A. Angelopoulos, S. Bates, J. Malik, and M. I. Jordan.
ICLR, 2021. (spotlight presentation)
[arXiv]
[video]
[blog]
[code]
[notebook]
[bibtex]
- “False discovery rate control in genome-wide association studies with population structure”
M. Sesia, S. Bates, E. Candès, J. Marchini, and C. Sabatti.
Proceedings of the National Academy of Sciences of the USA (PNAS), 2021.
[bioRxiv]
[journal]
[results+code]
[bibtex]
- “Achieving Equalized Odds by Resampling Sensitive Attributes”
Y. Romano, S. Bates, and E. Candès. NeurIPS, 2020.
[arXiv]
[code]
[bibtex]
- “Causal Inference in Genetic Trio Studies”
S. Bates, M. Sesia, C. Sabatti, and E. Candès.
Proceedings of the National Academy of Sciences of the USA (PNAS), 2020.
[arXiv]
[journal]
[video]
[tutorials+code]
[bibtex]
*Selected as a cover article and for invited commentary.
- “Multi-resolution Localization of Causal Variants Across the Genome”
M. Sesia, E. Katsevich, S. Bates, E. Candès, and C. Sabatti. Nature Communications, 2020.
[bioRxiv]
[journal]
[website]
[bibtex]
- “Metropolized Knockoff Sampling”
S. Bates, E. J. Candès, L. Janson, and W. Wang.
Journal of the American Statistical Association (JASA), 2020.
[arXiv]
[journal]
[tutorials]
[code]
[bibtex]
- “Log-ratio Lasso: Scalable, Sparse Estimation for Log-ratio Models”
S. Bates and R. Tibshirani. Biometrics, 2019.
[arXiv]
[journal]
[code]
[bibtex]
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