| A modern maximum-likelihood theory for high-dimensional logistic regression P Sur, EJ Candès Proceedings of the National Academy of Sciences 116 (29), 14516-14525, 2019 | 331 | 2019 |
| The likelihood ratio test in high-dimensional logistic regression is asymptotically a rescaled Chi-square P Sur, Y Chen, EJ Candès Probability theory and related fields 175, 487-558, 2019 | 154 | 2019 |
| The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression EJ Candès, P Sur The Annals of Statistics 48 (1), 27-42, 2020 | 141 | 2020 |
| A precise high-dimensional asymptotic theory for boosting and minimum--norm interpolated classifiers T Liang, P Sur The Annals of Statistics 50 (3), 1669-1695, 2022 | 80 | 2022 |
| The asymptotic distribution of the MLE in high-dimensional logistic models: Arbitrary covariance Q Zhao, P Sur, EJ Candes Bernoulli 28 (3), 1835-1861, 2022 | 36 | 2022 |
| Modeling bimodal discrete data using Conway-Maxwell-Poisson mixture models P Sur, G Shmueli, S Bose, P Dubey Journal of Business & Economic Statistics 33 (3), 352-365, 2015 | 29 | 2015 |
| Representation via representations: Domain generalization via adversarially learned invariant representations Z Deng, F Ding, C Dwork, R Hong, G Parmigiani, P Patil, P Sur arXiv preprint arXiv:2006.11478, 2020 | 21 | 2020 |
| A non-asymptotic moreau envelope theory for high-dimensional generalized linear models L Zhou, F Koehler, P Sur, DJ Sutherland, N Srebro Advances in Neural Information Processing Systems 35, 21286-21299, 2022 | 16 | 2022 |
| A new central limit theorem for the augmented ipw estimator: Variance inflation, cross-fit covariance and beyond K Jiang, R Mukherjee, S Sen, P Sur arXiv preprint arXiv:2205.10198, 2022 | 12 | 2022 |
| Abstracting fairness: Oracles, metrics, and interpretability C Dwork, C Ilvento, GN Rothblum, P Sur arXiv preprint arXiv:2004.01840, 2020 | 11 | 2020 |
| High-dimensional asymptotics of langevin dynamics in spiked matrix models T Liang, S Sen, P Sur arXiv preprint arXiv:2204.04476, 2022 | 9 | 2022 |
| Fitting Com-Poisson mixtures to bimodal count data S Bose, G Shmueli, P Sur, P Dubey 1st International Conference on Information, Operations Management and …, 2013 | 6 | 2013 |
| Additional supplementary materials for: a modern maximum-likelihood theory for high-dimensional logistic regression P Sur, EJ Candès | 4 | 2018 |
| Supplemental materials for “the likelihood ratio test in high-dimensional logistic regression is asymptotically a rescaled chi-square” P Sur, Y Chen, E Candès | 2 | 2017 |
| Spectrum-aware adjustment: A new debiasing framework with applications to principal components regression Y Li, P Sur arXiv preprint arXiv:2309.07810, 2023 | 1 | 2023 |
| Multi-Study Boosting: Theoretical Considerations for Merging vs. Ensembling C Shyr, P Sur, G Parmigiani, P Patil arXiv preprint arXiv:2207.04588, 2022 | 1 | 2022 |
| Supporting Information to: A Modern Maximum-Likelihood Theory for High-dimensional Logistic Regression P Sur, EJ Candès Proceedings of the National Academy of Sciences, 2018 | 1 | 2018 |
| Predictive Inference in Multi-environment Scenarios JC Duchi, S Gupta, K Jiang, P Sur arXiv preprint arXiv:2403.16336, 2024 | | 2024 |
| Universality in block dependent linear models with applications to nonparametric regression S Lahiry, P Sur arXiv preprint arXiv:2401.00344, 2023 | | 2023 |
| A precise high-dimensional asymptotic theory for boosting and minimum-l1-norm interpolated classifiers T Liang, P Sur The Annals of Statistics 50 (3), 1669-1695, 2022 | | 2022 |
Emmanuel CandesThe Simons Chair in Mathematics and Statistics, Stanford UniversityVerifierad e-postadress på stanford.edu
