Jiayu Yao
Jiayu Yao
School of Engineering and Applied Sciences, Harvard University
Verified email at g.harvard.edu
Title
Cited by
Cited by
Year
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
542016
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
482018
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
382018
Quality of uncertainty quantification for Bayesian neural network inference
J Yao, W Pan, S Ghosh, F Doshi-Velez
arXiv preprint arXiv:1906.09686, 2019
372019
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
132019
Direct policy transfer via hidden parameter markov decision processes
J Yao, T Killian, G Konidaris, F Doshi-Velez
LLARLA Workshop, FAIM 2018, 2018
122018
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
102019
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
92018
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
42018
Power-Constrained Bandits
J Yao, E Brunskill, W Pan, S Murphy, F Doshi-Velez
arXiv preprint arXiv:2004.06230, 2020
32020
Amortised Variational Inference for Hierarchical Mixture Models
J Antorán, J Yao, W Pan, JM Hernández-Lobato, F Doshi-Velez
Projected BNNs: Avoiding weight-space pathologies by projecting neural network weights
MF Pradier, W Pan, J Yao, S Ghosh, F Doshi-Velez
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Articles 1–12