Conference and Journal Papers


  1. Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations
    Mandyam, Aishwarya, Jones, Andrew,  Yao, Jiayu, Laudanski, Krzysztof, and Engelhardt, Barbara E
    In Machine Learning for Health (ML4H) 2023
  2. Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs
    Fu, Haotian,  Yao, Jiayu, Gottesman, Omer, Doshi-Velez, Finale, and Konidaris, George
    In The Eleventh International Conference on Learning Representations 2023


  1. Power Constrained Bandits
    Yao, Jiayu, Brunskill, Emma, Pan, Weiwei, Murphy, Susan, and Doshi-Velez, Finale
    In Machine Learning for Healthcare Conference 2021


  1. Model Selection in Bayesian Neural Networks via Horseshoe Priors.
    Ghosh, Soumya,  Yao, Jiayu, and Doshi-Velez, Finale
    JMLR 2019


  1. Structured variational learning of Bayesian neural networks with horseshoe priors
    Ghosh, Soumya,  Yao, Jiayu, and Doshi-Velez, Finale
    In International Conference on Machine Learning 2018


  1. Normal/abnormal heart sound recordings classification using convolutional neural network
    Nilanon, Tanachat,  Yao, Jiayu, Hao, Junheng, Purushotham, Sanjay, and Liu, Yan
    In 2016 computing in cardiology conference (CinC) 2016

Workshops and Preprints


  1. Inverse Reinforcement Learning with Multiple Planning Horizons
    Yao, Jiayu, Doshi-Velez, Finale, and Engelhardt, Barbara
    In NeurIPS 2023 Workshop on Generalization in Planning 2023


  1. An Empirical Analysis of the Advantages of Finite-vs Infinite-Width Bayesian Neural Networks
    Yao, Jiayu, Yacoby, Yaniv, Coker, Beau, Pan, Weiwei, and Doshi-Velez, Finale
    arXiv preprint arXiv:2211.09184 2022
  2. A Framework for the Evaluation of Clinical Time Series Models
    Gao, Michael,  Yao, Jiayu, and Henao, Ricardo
    In NeurIPS 2022 Workshop on Learning from Time Series for Health 2022
  3. Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry
    Penrod, Mark, Termotto, Harrison, Reddy, Varshini,  Yao, Jiayu, Doshi-Velez, Finale, and Pan, Weiwei
    arXiv preprint arXiv:2208.01705 2022
  4. From Soft Trees to Hard Trees: Gains and Losses
    Zeng, Xin,  Yao, Jiayu, Doshi-Velez, Finale, and Pan, Weiwei
  5. Policy Optimization with Sparse Global Contrastive Explanations
    Yao, Jiayu, Parbhoo, Sonali, Pan, Weiwei, and Doshi-Velez, Finale
    arXiv preprint arXiv:2207.06269 2022


  1. Amortised Variational Inference for Hierarchical Mixture Models
    Antorán, Javier,  Yao, Jiayu, Pan, Weiwei, Hernández-Lobato, José Miguel, and Doshi-Velez, Finale
    arXiv preprint arXiv:2111.03144 2020


  1. Output-constrained Bayesian neural networks
    Yang, Wanqian, Lorch, Lars, Graule, Moritz A, Srinivasan, Srivatsan, Suresh, Anirudh,  Yao, Jiayu, Pradier, Melanie F, and Doshi-Velez, Finale
    arXiv preprint arXiv:1905.06287 2019
  2. Quality of uncertainty quantification for Bayesian neural network inference
    Yao, Jiayu, Pan, Weiwei, Ghosh, Soumya, and Doshi-Velez, Finale
    arXiv preprint arXiv:1906.09686 2019


  1. Evaluating reinforcement learning algorithms in observational health settings
    Gottesman, Omer, Johansson, Fredrik, Meier, Joshua, Dent, Jack, Lee, Donghun, Srinivasan, Srivatsan, Zhang, Linying, Ding, Yi, Wihl, David, Peng, Xuefeng, and others,
    arXiv preprint arXiv:1805.12298 2018
  2. Direct policy transfer via hidden parameter markov decision processes
    Yao, Jiayu, Killian, Taylor, Konidaris, George, and Doshi-Velez, Finale
    In LLARLA Workshop, FAIM 2018
  3. Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights
    Pradier, Melanie F, Pan, Weiwei,  Yao, Jiayu, Ghosh, Soumya, and Doshi-Velez, Finale
    arXiv preprint arXiv:1811.07006 2018