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Finale Doshi-velez

54 papers · 2009–2025 · 12 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (15) πŸ—ΊοΈ Taxonomy Completionist (21) πŸƒ Academic Marathon (16) 🧭 Keyword Pioneer 🌍 Conference Polyglot (12) 🌟 Keyword Trendsetter Combo (3) πŸ† Keyword Champion πŸ”¬ Deep Specialist (11) πŸ† Grand Slam πŸ“ˆ Trend Setter πŸ’Ž Century Club (54) ⚑ Prolific Year (7) πŸ—ƒοΈ Keyword Collector (62) πŸ”₯ Unstoppable (11)

Conferences

NIPS (12) AISTATS (7) ICML (7) IJCAI (6) JMLR (6) AAAI (4) MLHC (4) ACL (2) ICLR (2) UAI (2) COLING (1) EMNLP (1)

Top co-authors

Sonali Parbhoo (9) Weiwei Pan (8) Omer Gottesman (7) Emma Brunskill (4) Jiayu Yao (4) Joseph Futoma (3) Lane Schwartz (3) Yao Liu (3) George Konidaris (3) Abhishek Sharma (3)

Research topics

Applications (1)

Keywords

reinforcement learning (8) bayesian neural network (8) variational inference (7) uncertainty quantification (5) unsupervised learning (4) off-policy evaluation (4) markov decision process (3) mobile health (3) model-based reinforcement learning (3) bayesian inference (3) grammar induction (3) feature selection (3) nonparametric bayesian (2) offline reinforcement learning (2) model compression (2) posterior distribution (2) deep reinforcement learning (2) semi-supervised learning (2) sequential decision making (2) inverse reinforcement learning (2)

Papers

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