Finale Doshi-velez

A Deployed Online Reinforcement Learning Algorithm in an Oral Health Clinical Trial AAAI 2025 Connecting Federated ADMM to Bayes ICLR 2025 Transparent Trade-offs between Properties of Explanations UAI 2025 Decision-Point Guided Safe Policy Improvement AISTATS 2025 XAI-Lyricist: Improving the Singability of AI-Generated Lyrics with Prosody Explanations IJCAI 2024 Decision-Focused Model-based Reinforcement Learning for Reward Transfer MLHC 2024 Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning JMLR 2024 The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning ICML 2023 Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs ICLR 2023 Reward Design for an Online Reinforcement Learning Algorithm Supporting Oral Self-Care AAAI 2023 Learning Optimal Summaries of Clinical Time-series with Concept Bottleneck Models MLHC 2022 Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables JMLR 2022 Wide Mean-Field Bayesian Neural Networks Ignore the Data AISTATS 2022 Addressing Leakage in Concept Bottleneck Models NIPS 2022 Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare NIPS 2022 A Joint Learning Approach for Semi-supervised Neural Topic Modeling ACL 2022 Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement ICML 2021 Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning NIPS 2021 State Relevance for Off-Policy Evaluation ICML 2021 Power Constrained Bandits MLHC 2021 Incorporating Interpretable Output Constraints in Bayesian Neural Networks NIPS 2020 Transfer Learning from Well-Curated to Less-Resourced Populations with HIV MLHC 2020 POPCORN: Partially Observed Prediction Constrained Reinforcement Learning AISTATS 2020 Ensembles of Locally Independent Prediction Models AAAI 2020 Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs NIPS 2020 Prediction Focused Topic Models via Feature Selection AISTATS 2020 Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions ICML 2020 PoRB-Nets: Poisson Process Radial Basis Function Networks UAI 2020 Regional Tree Regularization for Interpretability in Deep Neural Networks AAAI 2020 Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies IJCAI 2019 Exploring Computational User Models for Agent Policy Summarization IJCAI 2019 Truly Batch Apprenticeship Learning with Deep Successor Features IJCAI 2019 A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization JMLR 2019 Model Selection in Bayesian Neural Networks via Horseshoe Priors JMLR 2019 Unsupervised Learning of PCFGs with Normalizing Flow ACL 2019 Combining parametric and nonparametric models for off-policy evaluation ICML 2019 Weighted Tensor Decomposition for Learning Latent Variables with Partial Data AISTATS 2018 Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction EMNLP 2018 Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning ICML 2018 Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors ICML 2018 Representation Balancing MDPs for Off-policy Policy Evaluation NIPS 2018 Human-in-the-Loop Interpretability Prior NIPS 2018 Semi-Supervised Prediction-Constrained Topic Models AISTATS 2018 Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes NIPS 2017 A Bayesian Framework for Learning Rule Sets for Interpretable Classification JMLR 2017 Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations IJCAI 2017 Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input COLING 2016 Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders JMLR 2016 Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations IJCAI 2016 Spectral M-estimation with Applications to Hidden Markov Models AISTATS 2016 Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction NIPS 2015 Nonparametric Bayesian Policy Priors for Reinforcement Learning NIPS 2010 The Infinite Partially Observable Markov Decision Process NIPS 2009 Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process NIPS 2009