| The age of secrecy and unfairness in recidivism prediction C Rudin, C Wang, B Coker Harvard Data Science Review 2 (1), 1, 2020 | 186 | 2020 |
| A theory of statistical inference for ensuring the robustness of scientific results B Coker, C Rudin, G King Management Science 67 (10), 6174-6197, 2021 | 29 | 2021 |
| Wide mean-field bayesian neural networks ignore the data B Coker, WP Bruinsma, DR Burt, W Pan, F Doshi-Velez International Conference on Artificial Intelligence and Statistics, 5276-5333, 2022 | 19 | 2022 |
| Wide mean-field variational bayesian neural networks ignore the data B Coker, W Pan, F Doshi-Velez arXiv preprint arXiv:2106.07052, 2021 | 8 | 2021 |
| Broader issues surrounding model transparency in criminal justice risk scoring C Rudin, C Wang, B Coker Harvard Data Science Review 2 (1), 2020 | 8 | 2020 |
| The age of secrecy and unfairness in recidivism prediction (2018) C Rudin, C Wang, B Coker arXiv preprint arXiv:1811.00731, 1811 | 8 | 1811 |
| The Age of Secrecy and Unfairness in Recidivism Prediction. Harvard Data Science Review 2, 1 (31 3 2020) C Rudin, C Wang, B Coker | 7 | 2020 |
| Differentially private survey research G Evans, G King, AD Smith, A Thakurta, J Katz, G King, E Rosenblatt, ... American Journal of Political Science 28, 1-22, 2022 | 5 | 2022 |
| Towards expressive priors for Bayesian neural networks: Poisson process radial basis function networks B Coker, MF Pradier, F Doshi-Velez arXiv preprint arXiv:1912.05779, 2019 | 5 | 2019 |
| Learning a latent space of highly multidimensional cancer data B Kompa, B Coker PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020, 379-390, 2019 | 5 | 2019 |
| The age of secrecy and unfairness in recidivism prediction. Harvard Data Science Review, 2 (1), 3 2020 C Rudin, C Wang, B Coker | 5 | |
| Porb-nets: Poisson process radial basis function networks B Coker, MF Pradier, F Doshi-Velez Conference on Uncertainty in Artificial Intelligence, 1338-1347, 2020 | 3 | 2020 |
| Towards a unified framework for uncertainty-aware nonlinear variable selection with theoretical guarantees W Deng, B Coker, R Mukherjee, J Liu, B Coull Advances in Neural Information Processing Systems 35, 27636-27651, 2022 | 2 | 2022 |
| An empirical analysis of the advantages of finite-vs infinite-width bayesian neural networks J Yao, Y Yacoby, B Coker, W Pan, F Doshi-Velez arXiv preprint arXiv:2211.09184, 2022 | 2 | 2022 |
| Implications of Gaussian process kernel mismatch for out-of-distribution data B Coker, F Doshi-Velez ICML 2023 Workshop on Structured Probabilistic Inference {\&} Generative …, 2023 | | 2023 |
| Misspecification, Nonstationarity, and Approximate Inference in Gaussian Processes and Bayesian Neural Networks B Coker Harvard University, 2023 | | 2023 |
| Learning a Generative Model of Cancer Metastasis B Kompa, B Coker arXiv preprint arXiv:1901.06023, 2019 | | 2019 |
| Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees (with Supplementary Material) W Deng, B Coker, R Mukherjee, JZ Liu, BA Coull | | |
