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Beau Coker
Beau Coker
PhD candidate in Biostatistics, Harvard University
Verified email at g.harvard.edu - Homepage
Title
Cited by
Cited by
Year
The age of secrecy and unfairness in recidivism prediction
C Rudin, C Wang, B Coker
Harvard Data Science Review 2 (1), 1, 2020
1782020
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
262021
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
182022
Wide mean-field variational bayesian neural networks ignore the data
B Coker, W Pan, F Doshi-Velez
arXiv preprint arXiv:2106.07052, 2021
92021
Broader Issues Surrounding Model Transparency in Criminal Justice Risk Scoring
C Rudin, C Wang, B Coker
Harvard Data Science Review 2 (1), 2020
82020
The age of secrecy and unfairness in recidivism prediction (2018)
C Rudin, C Wang, B Coker
arXiv preprint arXiv:1811.00731, 1811
81811
The Age of Secrecy and Unfairness in Recidivism Prediction. Harvard Data Science Review 2, 1 (31 3 2020)
C Rudin, C Wang, B Coker
72020
The age of secrecy and unfairness in recidivism prediction. Harvard Data Science Review, 2 (1), 3 2020
C Rudin, C Wang, B Coker
6
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
52019
Learning a latent space of highly multidimensional cancer data
B Kompa, B Coker
PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020, 379-390, 2019
52019
Differentially private survey research
G Evans, G King, AD Smith, A Thakurta, J Katz, G King, E Rosenblatt, ...
American Journal of Political Science 27, 703-709, 2022
42022
Porb-nets: Poisson process radial basis function networks
B Coker, MF Pradier, F Doshi-Velez
Conference on Uncertainty in Artificial Intelligence, 1338-1347, 2020
32020
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
22022
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
22022
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
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
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