| Evaluating reinforcement learning algorithms in observational health settings O Gottesman, F Johansson, J Meier, J Dent, D Lee, S Srinivasan, L Zhang, ... arXiv preprint arXiv:1805.12298, 2018 | 145* | 2018 |
| Quality of uncertainty quantification for Bayesian neural network inference J Yao, W Pan, S Ghosh, F Doshi-Velez arXiv preprint arXiv:1906.09686, 2019 | 123 | 2019 |
| Normal/abnormal heart sound recordings classification using convolutional neural network T Nilanon, J Yao, J Hao, S Purushotham, Y Liu 2016 computing in cardiology conference (CinC), 585-588, 2016 | 119 | 2016 |
| Structured variational learning of Bayesian neural networks with horseshoe priors S Ghosh, J Yao, F Doshi-Velez International Conference on Machine Learning, 1744-1753, 2018 | 89 | 2018 |
| Model selection in Bayesian neural networks via horseshoe priors S Ghosh, J Yao, F Doshi-Velez Journal of Machine Learning Research 20 (182), 1-46, 2019 | 84 | 2019 |
| Power constrained bandits J Yao, E Brunskill, W Pan, S Murphy, F Doshi-Velez Machine Learning for Healthcare Conference, 209-259, 2021 | 35 | 2021 |
| Direct policy transfer via hidden parameter markov decision processes J Yao, T Killian, G Konidaris, F Doshi-Velez LLARLA Workshop, FAIM 2018, 2018 | 31 | 2018 |
| Output-constrained Bayesian neural networks W Yang, L Lorch, MA Graule, S Srinivasan, A Suresh, J Yao, MF Pradier, ... arXiv preprint arXiv:1905.06287, 2019 | 23 | 2019 |
| Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights MF Pradier, W Pan, J Yao, S Ghosh, F Doshi-Velez arXiv preprint arXiv:1811.07006, 2018 | 11 | 2018 |
| Latent projection bnns: Avoiding weight-space pathologies by learning latent representations of neural network weights MF Pradier, W Pan, J Yao, S Ghosh, F Doshi-Velez Workshop on Bayesian Deep Learning, NIPS, 2018 | 11 | 2018 |
| Policy optimization with sparse global contrastive explanations J Yao, S Parbhoo, W Pan, F Doshi-Velez arXiv preprint arXiv:2207.06269, 2022 | 3 | 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 |
| Performance bounds for model and policy transfer in hidden-parameter mdps H Fu, J Yao, O Gottesman, F Doshi-Velez, G Konidaris The Eleventh International Conference on Learning Representations, 2022 | 2 | 2022 |
| Inverse Reinforcement Learning with Multiple Planning Horizons J Yao, F Doshi-Velez, B Engelhardt NeurIPS 2023 Workshop on Generalization in Planning, 2023 | | 2023 |
| Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations A Mandyam, A Jones, J Yao, K Laudanski, BE Engelhardt Machine Learning for Health (ML4H), 323-339, 2023 | | 2023 |
| A Framework for the Evaluation of Clinical Time Series Models M Gao, J Yao, R Henao NeurIPS 2022 Workshop on Learning from Time Series for Health, 2022 | | 2022 |
| Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry M Penrod, H Termotto, V Reddy, J Yao, F Doshi-Velez, W Pan arXiv preprint arXiv:2208.01705, 2022 | | 2022 |
| Reinforcement Learning for Healthcare: From Model Development to Deployment J Yao Harvard University, 2022 | | 2022 |
| Projected BNNs: Avoiding weight-space pathologies by projecting neural network weights MF Pradier, W Pan, J Yao, S Ghosh, F Doshi-Velez | | |
| From Soft Trees to Hard Trees: Gains and Losses X Zeng, J Yao, F Doshi-Velez, W Pan | | |
