Lage I, Doshi-Velez F.
Human-in-the-Loop Learning of Interpretable and Intuitive Representations. ICML Workshop on Human Interpretability in Machine Learning, . 2020;1 :1-10.
Paper Yao J, Brunskill E, Pan W, Murphy S, Doshi-Velez F.
Power-Constrained Bandits. ICML Workshop on Theoretical Foundations of Reinforcement Learning. 2020;2 :1-30.
Paper Nair Y, Doshi-Velez F.
PAC Imitation and Model-based Batch Learning of Contextual MDPs. ICML Workshop on Theoretical Foundations of Reinforcement Learning. 2020;2 :1-21.
Paper Nair Y, Doshi-Velez F.
PAC Imitation and Model-based Batch Learning of Contextual MDPs. ICML Workshop on Inductive Biases, Invariances and Generalization in RL. 2020;2 :1-21.
Paper Thakur S, Lorsung C, Yacoby Y, Doshi-Velez F, Pan W.
Learned Uncertainty-Aware (LUNA) Bases for Bayesian Regression using Multi-Headed Auxiliary Networks. ICML Workshop on Uncertainty in Deep Learning. 2020;2 :1-18.
Paper Gottesman O, Futoma J, Liu Y, Parbhoo S, Celi LA, Brunskill E, Doshi-Velez F.
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. International Conference on Machine Learning (IMCL). 2020;2 :1-17.
Paper Yacoby Y, Pan W, Doshi-Velez F.
Failures of Variational Autoencoders and their Effects on Downstream Tasks. ICML Workshop on Uncertainty in Deep Learning. 2020;1 :1-39.
Paper M. Downs, J. Chu, Yacoby Y, Doshi-Velez F, WeiWei P.
CRUDS: Counterfactual Recourse Using Disentangled Subspaces. ICML Workshop on Human Interpretability in Machine Learning. 2020 :1-23.
Paper Guenais T, Vamvourellis D, Yacoby Y, Doshi-Velez F, Pan W.
BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty. ICML Workshop on Uncertainty in Deep Learning. 2020;1 :1-24.
Paper J. Antoran, Yao J, Pan W, Doshi-Velez F, Hernandez-Lobato J.
Amortised Variational Inference for Hierarchical Mixture Models. ICML Workshop on Uncertainty in Deep Learning. 2020 :1-11.
Paper Yacoby Y, Pan W, Doshi-Velez F.
Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders. Advances in Approximate Bayesian Inference. 2020;1 :1-17.
Paper Prasad N, Engelhardt B, Doshi-Velez F.
Defining Admissible Rewards for High-Confidence Policy Evaluation in Batch Reinforcement Learning. ACM Conference on Health, Inference and Learning. 2020;2 :1-9.
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