| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 952 | 2023 |
| Underspecification presents challenges for credibility in modern machine learning A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... Journal of Machine Learning Research 23 (226), 1-61, 2022 | 722 | 2022 |
| Clinical implications of revised pooled cohort equations for estimating atherosclerotic cardiovascular disease risk S Yadlowsky, RA Hayward, JB Sussman, RL McClelland, YI Min, S Basu Annals of internal medicine 169 (1), 20-29, 2018 | 199 | 2018 |
| Counterfactual invariance to spurious correlations in text classification V Veitch, A D'Amour, S Yadlowsky, J Eisenstein Advances in neural information processing systems 34, 16196-16208, 2021 | 149 | 2021 |
| Bounds on the conditional and average treatment effect with unobserved confounding factors S Yadlowsky, H Namkoong, S Basu, J Duchi, L Tian Annals of statistics 50 (5), 2587, 2022 | 88* | 2022 |
| The multiberts: Bert reproductions for robustness analysis T Sellam, S Yadlowsky, J Wei, N Saphra, A D'Amour, T Linzen, J Bastings, ... arXiv preprint arXiv:2106.16163, 2021 | 80 | 2021 |
| Off-policy policy evaluation for sequential decisions under unobserved confounding H Namkoong, R Keramati, S Yadlowsky, E Brunskill Advances in Neural Information Processing Systems 33, 18819-18831, 2020 | 68 | 2020 |
| Deep cox mixtures for survival regression C Nagpal, S Yadlowsky, N Rostamzadeh, K Heller Machine Learning for Healthcare Conference, 674-708, 2021 | 61 | 2021 |
| Evaluating treatment prioritization rules via rank-weighted average treatment effects S Yadlowsky, S Fleming, N Shah, E Brunskill, S Wager arXiv preprint arXiv:2111.07966, 2021 | 59 | 2021 |
| Cellpath: Fusion of cellular and traffic sensor data for route flow estimation via convex optimization C Wu, J Thai, S Yadlowsky, A Pozdnoukhov, A Bayen Transportation Research Procedia 7, 212-232, 2015 | 54 | 2015 |
| Adaptive sampling probabilities for non-smooth optimization H Namkoong, A Sinha, S Yadlowsky, JC Duchi International Conference on Machine Learning, 2574-2583, 2017 | 46 | 2017 |
| Estimation and validation of ratio-based conditional average treatment effects using observational data S Yadlowsky, F Pellegrini, F Lionetto, S Braune, L Tian Journal of the American Statistical Association 116 (533), 335-352, 2021 | 23 | 2021 |
| Measure what matters: counts of hospitalized patients are a better metric for health system capacity planning for a reopening S Kashyap, S Gombar, S Yadlowsky, A Callahan, J Fries, BA Pinsky, ... Journal of the American Medical Informatics Association 27 (7), 1026-1131, 2020 | 22 | 2020 |
| Pretraining data mixtures enable narrow model selection capabilities in transformer models S Yadlowsky, L Doshi, N Tripuraneni arXiv preprint arXiv:2311.00871, 2023 | 21 | 2023 |
| Derivative free optimization via repeated classification T Hashimoto, S Yadlowsky, J Duchi International Conference on Artificial Intelligence and Statistics, 2027-2036, 2018 | 20 | 2018 |
| Diagnosing model performance under distribution shift TT Cai, H Namkoong, S Yadlowsky arXiv preprint arXiv:2303.02011, 2023 | 16 | 2023 |
| Sloe: A faster method for statistical inference in high-dimensional logistic regression S Yadlowsky, T Yun, CY McLean, A D'Amour Advances in neural information processing systems 34, 29517-29528, 2021 | 14 | 2021 |
| A calibration metric for risk scores with survival data S Yadlowsky, S Basu, L Tian Machine Learning for Healthcare Conference, 424-450, 2019 | 12 | 2019 |
| Calibration error for heterogeneous treatment effects Y Xu, S Yadlowsky International Conference on Artificial Intelligence and Statistics, 9280-9303, 2022 | 9 | 2022 |
| Explaining Practical Differences Between Treatment Effect Estimators with High Dimensional Asymptotics S Yadlowsky arXiv preprint arXiv:2203.12538, 2022 | 8* | 2022 |
Sanjay Basu, MD, PhD在 hms.harvard.edu 的电子邮件经过验证
