Removing hidden confounding by experimental grounding N Kallus, AM Puli, U Shalit Advances in neural information processing systems 31, 2018 | 159 | 2018 |
Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations A Puli, LH Zhang, EK Oermann, R Ranganath ICLR 2022, arXiv preprint arXiv:2107.00520, 2021 | 55* | 2021 |
X-cal: Explicit calibration for survival analysis M Goldstein, X Han, A Puli, A Perotte, R Ranganath Advances in neural information processing systems 33, 18296-18307, 2020 | 44 | 2020 |
General control functions for causal effect estimation from ivs A Puli, R Ranganath Advances in neural information processing systems 33, 8440-8451, 2020 | 23* | 2020 |
Don’t blame dataset shift! shortcut learning due to gradients and cross entropy AM Puli, L Zhang, Y Wald, R Ranganath Advances in Neural Information Processing Systems 36, 2023 | 21 | 2023 |
When more is less: Incorporating additional datasets can hurt performance by introducing spurious correlations R Compton, L Zhang, A Puli, R Ranganath Machine Learning for Healthcare Conference, 110-127, 2023 | 17 | 2023 |
Nuisances via negativa: Adjusting for spurious correlations via data augmentation A Puli, N Joshi, Y Wald, H He, R Ranganath TMLR 2024, arXiv preprint arXiv:2210.01302, 2022 | 14 | 2022 |
Inverse-Weighted Survival Games M Goldstein, X Han, A Puli, T Wies, A Perotte, R Ranganath NeurIPS (cit. on p. 60), 2021 | 14* | 2021 |
Causal Estimation with Functional Confounders A Puli, AJ Perotte, R Ranganath Advances in neural information processing systems 33, 5115, 2020 | 11 | 2020 |
Beyond distribution shift: Spurious features through the lens of training dynamics N Murali, A Puli, K Yu, R Ranganath, K Batmanghelich Transactions on machine learning research 2023, https://openreview. net …, 2023 | 8* | 2023 |
Learning invariant representations with missing data M Goldstein, JH Jacobsen, O Chau, A Saporta, AM Puli, R Ranganath, ... Conference on Causal Learning and Reasoning, 290-301, 2022 | 8 | 2022 |
Individual treatment effect estimation in the presence of unobserved confounding using proxies: a cohort study in stage III non-small cell lung cancer WAC van Amsterdam, JJC Verhoeff, NI Harlianto, GA Bartholomeus, ... Scientific reports 12 (1), 5848, 2022 | 8 | 2022 |
Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning Y Hu, A Lui, M Goldstein, M Sudarshan, A Tinsay, C Tsui, SD Maidman, ... European Heart Journal: Acute Cardiovascular Care 13 (6), 472-480, 2024 | 7 | 2024 |
Contra: Contrarian statistics for controlled variable selection M Sudarshan, A Puli, L Subramanian, S Sankararaman, R Ranganath International conference on artificial intelligence and statistics, 1900-1908, 2021 | 4 | 2021 |
DIET: Conditional independence testing with marginal dependence measures of residual information M Sudarshan, A Puli, W Tansey, R Ranganath International Conference on Artificial Intelligence and Statistics, 10343-10367, 2023 | 3 | 2023 |
New-onset diabetes assessment using artificial intelligence-enhanced electrocardiography N Jethani, A Puli, H Zhang, L Garber, L Jankelson, Y Aphinyanaphongs, ... arXiv preprint arXiv:2205.02900, 2022 | 3 | 2022 |
Robust anomaly detection for particle physics using multi-background representation learning A Gandrakota, LH Zhang, A Puli, K Cranmer, J Ngadiuba, R Ranganath, ... Machine Learning: Science and Technology 5 (3), 035082, 2024 | 2 | 2024 |
Bayesian Modeling of Marketing Attribution R Sinha, D Arbour, AM Puli arXiv preprint arXiv:2205.15965, 2022 | 2 | 2022 |
Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities A Saporta, AM Puli, M Goldstein, R Ranganath Advances in Neural Information Processing Systems 37, 56919-56957, 2024 | 1 | 2024 |
A dynamic risk score for early prediction of cardiogenic shock using machine learning Y Hu, A Lui, M Goldstein, M Sudarshan, A Tinsay, C Tsui, S Maidman, ... arXiv preprint arXiv:2303.12888, 2023 | 1 | 2023 |