Efficient Model Learning for Dialog Management. - Finale Doshi; Nicholas Roy (AAAI Spring Symposium: Multidisciplinary Collaboration for Socially Assistive Robotics, 2007)

Collision detection in legged locomotion using supervised learning. - Finale Doshi; Emma Brunskill; Alexander C. Shkolnik; Thomas Kollar; Khashayar Rohanimanesh; Russ Tedrake; Nicholas Roy (IROS, 2007)

The permutable POMDP: fast solutions to POMDPs for preference elicitation. - Finale Doshi; Nicholas Roy (AAMAS (1), 2008)

Spoken language interaction with model uncertainty: an adaptive human-robot interaction system. - Finale Doshi; Nicholas Roy (Connect. Sci., 2008)

Accelerated sampling for the Indian Buffet Process. - Finale Doshi-Velez; Zoubin Ghahramani (ICML, 2009)

The Infinite Partially Observable Markov Decision Process. - Finale Doshi-Velez (NIPS, 2009)

Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process. - Finale Doshi-Velez; David A. Knowles; Shakir Mohamed; Zoubin Ghahramani (NIPS, 2009)

Correlated Non-Parametric Latent Feature Models. - Finale Doshi-Velez; Zoubin Ghahramani (UAI, 2009)

Variational Inference for the Indian Buffet Process. - Finale Doshi; Kurt Miller; Jurgen Van Gael; Yee Whye Teh (AISTATS, 2009)

Nonparametric Bayesian Approaches for Reinforcement Learning in Partially Observable Domains. - Finale Doshi-Velez (AAAI, 2010)

A Bayesian Nonparametric Approach to Modeling Mobility Patterns. - Joshua Mason Joseph; Finale Doshi-Velez; Nicholas Roy (AAAI, 2010)

Nonparametric Bayesian Policy Priors for Reinforcement Learning. - Finale Doshi-Velez; David Wingate; Nicholas Roy; Joshua B. Tenenbaum (NIPS, 2010)

Organizing Committee. - Mark Buller; Paul Cuddihy; Finale Doshi-Velez (AAAI Spring Symposium: Computational Physiology, 2011)

A Comparison of Human and Agent Reinforcement Learning in Partially Observable Domains. - Finale Doshi-Velez; Zoubin Ghahramani (CogSci, 2011)

Infinite Dynamic Bayesian Networks. - Finale Doshi; David Wingate; Joshua B. Tenenbaum; Nicholas Roy (ICML, 2011)

Online Discovery of Feature Dependencies. - Alborz Geramifard; Finale Doshi; Josh Redding; Nicholas Roy; Jonathan P. How (ICML, 2011)

Reports of the AAAI 2011 Spring Symposia. - Mark Buller; Paul Cuddihy; Ernest Davis; Patrick Doherty 001; Finale Doshi-Velez; Esra Erdem 001; Douglas H. Fisher; Nancy L. Green; Knut Hinkelmann; Mary Lou Maher; James McLurkin; Rajiv T. Maheswaran; et al. (AI Mag., 2011)

A Bayesian nonparametric approach to modeling motion patterns. - Joshua Mason Joseph; Finale Doshi-Velez; Albert S. Huang; Nicholas Roy (Auton. Robots, 2011)

A Bayesian nonparametric approach to modeling battery health. - Joshua Mason Joseph; Finale Doshi-Velez; Nicholas Roy (ICRA, 2012)

Reinforcement learning with limited reinforcement: Using Bayes risk for active learning in POMDPs. - Finale Doshi-Velez; Joelle Pineau; Nicholas Roy (Artif. Intell., 2012)

Bayesian nonparametric methods for reinforcement learning in partially observable domains. - Finale Doshi-Velez (2012)

Preface. - Finale Doshi-Velez; David C. Kale; Byron C. Wallace; Jenna Wiens (AAAI Workshop: Modern Artificial Intelligence for Health Analytics, 2014)

Hidden Parameter Markov Decision Processes: An Emerging Paradigm for Modeling Families of Related Tasks. - George Dimitri Konidaris; Finale Doshi-Velez (AAAI Fall Symposia, 2014)

Unfolding physiological state: mortality modelling in intensive care units. - Marzyeh Ghassemi; Tristan Naumann; Finale Doshi-Velez; Nicole Brimmer; Rohit Joshi; Anna Rumshisky; Peter Szolovits (KDD, 2014)

Graph-Sparse LDA: A Topic Model with Structured Sparsity. - Finale Doshi-Velez; Byron C. Wallace; Ryan P. Adams (AAAI, 2015)

Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction. - Been Kim; Julie A. Shah; Finale Doshi-Velez (NIPS, 2015)

Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems. - Tong Wang 011; Cynthia Rudin; Finale Doshi-Velez; Yimin Liu; Erica Klampfl; Perry MacNeille (CoRR, 2015)

Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning. - Finale Doshi-Velez; David Pfau; Frank D. Wood; Nicholas Roy (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Spectral M-estimation with Applications to Hidden Markov Models. - Dustin Tran; Minjae Kim; Finale Doshi-Velez (AISTATS, 2016)

Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input. - Cory Shain; William Bryce; Lifeng Jin; Victoria Krakovna; Finale Doshi-Velez; Timothy Miller 002; William Schuler; Lane Schwartz (COLING, 2016)

Bayesian Rule Sets for Interpretable Classification. - Tong Wang 011; Cynthia Rudin; Finale Doshi-Velez; Yimin Liu; Erica Klampfl; Perry MacNeille (ICDM, 2016)

Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations. - Finale Doshi-Velez; George Dimitri Konidaris (IJCAI, 2016)

Cost-Sensitive Batch Mode Active Learning: Designing Astronomical Observation by Optimizing Telescope Time and Telescope Choice. - Xide Xia; Pavlos Protopapas; Finale Doshi-Velez (SDM, 2016)

Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes. - Taylor W. Killian; George Dimitri Konidaris; Finale Doshi-Velez (CoRR, 2016)

Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models. - Viktoriya Krakovna; Finale Doshi-Velez (CoRR, 2016)

An Empirical Comparison of Sampling Quality Metrics: A Case Study for Bayesian Nonnegative Matrix Factorization. - Arjumand Masood; Weiwei Pan; Finale Doshi-Velez (CoRR, 2016)

A Characterization of the Non-Uniqueness of Nonnegative Matrix Factorizations. - Weiwei Pan; Finale Doshi-Velez (CoRR, 2016)

Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders. - Huseyin Melih Elibol; Vincent Nguyen; Scott W. Linderman; Matthew J. Johnson 002; Amna Hashmi; Finale Doshi-Velez (J. Mach. Learn. Res., 2016)

Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes. - Taylor W. Killian; George Dimitri Konidaris; Finale Doshi-Velez (AAAI, 2017)

Predicting intervention onset in the ICU with switching state space models. - Marzyeh Ghassemi; Mike Wu; Michael C. Hughes; Peter Szolovits; Finale Doshi-Velez (CRI, 2017)

Combining Kernel and Model Based Learning for HIV Therapy Selection. - Sonali Parbhoo; Jasmina Bogojeska; Maurizio Zazzi; Volker Roth 001; Finale Doshi-Velez (CRI, 2017)

Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. - Stefan Depeweg; José Miguel Hernández-Lobato; Finale Doshi-Velez; Steffen Udluft (ICLR (Poster), 2017)

Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. - Andrew Slavin Ross; Michael C. Hughes; Finale Doshi-Velez (IJCAI, 2017)

Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes. - Taylor W. Killian; Samuel Daulton; Finale Doshi-Velez; George Dimitri Konidaris (NIPS, 2017)

A Roadmap for a Rigorous Science of Interpretability. - Finale Doshi-Velez; Been Kim (CoRR, 2017)

Promoting Domain-Specific Terms in Topic Models with Informative Priors. - Angela Fan; Finale Doshi-Velez; Luke Miratrix (CoRR, 2017)

Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models. - Michael C. Hughes; Leah Weiner; Gabriel Hope; Thomas H. McCoy Jr.; Roy H. Perlis; Erik B. Sudderth; Finale Doshi-Velez (CoRR, 2017)

Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems. - Stefan Depeweg; José Miguel Hernández-Lobato; Finale Doshi-Velez; Steffen Udluft (CoRR, 2017)

Accountability of AI Under the Law: The Role of Explanation. - Finale Doshi-Velez; Mason Kortz; Ryan Budish; Chris Bavitz; Sam Gershman; David O'Brien; Stuart Schieber; James Waldo; David Weinberger; Alexandra Wood (CoRR, 2017)

Prediction-Constrained Topic Models for Antidepressant Recommendation. - Michael C. Hughes; Gabriel Hope; Leah Weiner; Thomas H. McCoy Jr.; Roy H. Perlis; Erik B. Sudderth; Finale Doshi-Velez (CoRR, 2017)

Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database. - Mike Wu; Marzyeh Ghassemi; Mengling Feng; Leo A. Celi; Peter Szolovits; Finale Doshi-Velez (J. Am. Medical Informatics Assoc., 2017)

A Bayesian Framework for Learning Rule Sets for Interpretable Classification. - Tong Wang 011; Cynthia Rudin; Finale Doshi-Velez; Yimin Liu; Erica Klampfl; Perry MacNeille (J. Mach. Learn. Res., 2017)

Restricted Indian buffet processes. - Finale Doshi-Velez; Sinead A. Williamson (Stat. Comput., 2017)

Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients. - Andrew Slavin Ross; Finale Doshi-Velez (AAAI, 2018)

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. - Mike Wu; Michael C. Hughes; Sonali Parbhoo; Maurizio Zazzi; Volker Roth 001; Finale Doshi-Velez (AAAI, 2018)

Weighted Tensor Decomposition for Learning Latent Variables with Partial Data. - Omer Gottesman; Weiwei Pan; Finale Doshi-Velez (AISTATS, 2018)

Semi-Supervised Prediction-Constrained Topic Models. - Michael C. Hughes; Gabriel Hope; Leah Weiner; Thomas H. McCoy Jr.; Roy H. Perlis; Erik B. Sudderth; Finale Doshi-Velez (AISTATS, 2018)

Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. - Xuefeng Peng; Yi Ding; David Wihl; Omer Gottesman; Matthieu Komorowski; Li-Wei H. Lehman; Andrew Slavin Ross; Aldo Faisal; Finale Doshi-Velez (AMIA, 2018)

Agent Strategy Summarization. - Ofra Amir; Finale Doshi-Velez; David Sarne (AAMAS, 2018)

Depth-bounding is effective: Improvements and Evaluation of Unsupervised PCFG Induction. - Lifeng Jin; Finale Doshi-Velez; Timothy Miller 002; William Schuler; Lane Schwartz (EMNLP, 2018)

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning. - Stefan Depeweg; José Miguel Hernández-Lobato; Finale Doshi-Velez; Steffen Udluft (ICML, 2018)

Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors. - Soumya Ghosh; Jiayu Yao; Finale Doshi-Velez (ICML, 2018)

Human-in-the-Loop Interpretability Prior. - Isaac Lage; Andrew Slavin Ross; Samuel J. Gershman; Been Kim; Finale Doshi-Velez (NeurIPS, 2018)

Representation Balancing MDPs for Off-policy Policy Evaluation. - Yao Liu 009; Omer Gottesman; Aniruddh Raghu; Matthieu Komorowski; Aldo A. Faisal; Finale Doshi-Velez; Emma Brunskill (NeurIPS, 2018)

How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation. - Menaka Narayanan; Emily Chen; Jeffrey He; Been Kim; Sam Gershman; Finale Doshi-Velez (CoRR, 2018)

Evaluating Reinforcement Learning Algorithms in Observational Health Settings. - Omer Gottesman; Fredrik D. Johansson; Joshua Meier; Jack Dent; Donghun Lee; Srivatsan Srinivasan; Linying Zhang; Yi Ding; David Wihl; Xuefeng Peng; Jiayu Yao; Isaac Lage; et al. (CoRR, 2018)

Learning Qualitatively Diverse and Interpretable Rules for Classification. - Andrew Slavin Ross; Weiwei Pan; Finale Doshi-Velez (CoRR, 2018)

Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters. - Aniruddh Raghu; Omer Gottesman; Yao Liu 009; Matthieu Komorowski; Aldo Faisal; Finale Doshi-Velez; Emma Brunskill (CoRR, 2018)

Latent Projection BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights. - Melanie F. Pradier; Weiwei Pan; Jiayu Yao; Soumya Ghosh; Finale Doshi-Velez (CoRR, 2018)

Unsupervised Grammar Induction with Depth-bounded PCFG. - Lifeng Jin; Finale Doshi-Velez; Timothy Miller 002; William Schuler; Lane Schwartz (Trans. Assoc. Comput. Linguistics, 2018)

PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models. - Michael Glueck; Mahdi Pakdaman Naeini; Finale Doshi-Velez; Fanny Chevalier; Azam Khan; Daniel Wigdor; Michael Brudno (IEEE Trans. Vis. Comput. Graph., 2018)

Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders. - Yaniv Yacoby; Weiwei Pan; Finale Doshi-Velez (AABI, 2019)

Unsupervised Learning of PCFGs with Normalizing Flow. - Lifeng Jin; Finale Doshi-Velez; Timothy Miller 002; Lane Schwartz; William Schuler (ACL (1), 2019)

Toward Robust Policy Summarization. - Isaac Lage; Daphna Lifschitz; Finale Doshi-Velez; Ofra Amir (AAMAS, 2019)

Human Evaluation of Models Built for Interpretability. - Isaac Lage; Emily Chen; Jeffrey He; Menaka Narayanan; Been Kim; Samuel J. Gershman; Finale Doshi-Velez (HCOMP, 2019)

Combining parametric and nonparametric models for off-policy evaluation. - Omer Gottesman; Yao Liu 009; Scott Sussex; Emma Brunskill; Finale Doshi-Velez (ICML, 2019)

Exploring Computational User Models for Agent Policy Summarization. - Isaac Lage; Daphna Lifschitz; Finale Doshi-Velez; Ofra Amir (IJCAI, 2019)

Truly Batch Apprenticeship Learning with Deep Successor Features. - Donghun Lee; Srivatsan Srinivasan; Finale Doshi-Velez (IJCAI, 2019)

Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies. - Muhammad A. Masood; Finale Doshi-Velez (IJCAI, 2019)

Summarizing agent strategies. - Ofra Amir; Finale Doshi-Velez; David Sarne (Auton. Agents Multi Agent Syst., 2019)

An Evaluation of the Human-Interpretability of Explanation. - Isaac Lage; Emily Chen; Jeffrey He; Menaka Narayanan; Been Kim; Sam Gershman; Finale Doshi-Velez (CoRR, 2019)

Output-Constrained Bayesian Neural Networks. - Wanqian Yang; Lars Lorch; Moritz A. Graule; Srivatsan Srinivasan; Anirudh Suresh; Jiayu Yao; Melanie F. Pradier; Finale Doshi-Velez (CoRR, 2019)

A general method for regularizing tensor decomposition methods via pseudo-data. - Omer Gottesman; Weiwei Pan; Finale Doshi-Velez (CoRR, 2019)

Defining Admissible Rewards for High Confidence Policy Evaluation. - Niranjani Prasad; Barbara E. Engelhardt; Finale Doshi-Velez (CoRR, 2019)

Quality of Uncertainty Quantification for Bayesian Neural Network Inference. - Jiayu Yao; Weiwei Pan; Soumya Ghosh; Finale Doshi-Velez (CoRR, 2019)

Regional Tree Regularization for Interpretability in Black Box Models. - Mike Wu; Sonali Parbhoo; Michael C. Hughes; Ryan Kindle; Leo A. Celi; Maurizio Zazzi; Volker Roth 001; Finale Doshi-Velez (CoRR, 2019)

Prediction Focused Topic Models via Vocab Selection. - Jason Ren; Russell Kunes; Finale Doshi-Velez (CoRR, 2019)

Learning Deep Bayesian Latent Variable Regression Models that Generalize: When Non-identifiability is a Problem. - Yaniv Yacoby; Weiwei Pan; Finale Doshi-Velez (CoRR, 2019)

Prediction Focused Topic Models for Electronic Health Records. - Jason Ren; Russell Kunes; Finale Doshi-Velez (CoRR, 2019)

Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks. - Beau Coker; Melanie F. Pradier; Finale Doshi-Velez (CoRR, 2019)

Model Selection in Bayesian Neural Networks via Horseshoe Priors. - Soumya Ghosh; Jiayu Yao; Finale Doshi-Velez (J. Mach. Learn. Res., 2019)

A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization. - Muhammad A. Masood; Finale Doshi-Velez (J. Mach. Learn. Res., 2019)

Assessing topic model relevance: Evaluation and informative priors. - Angela Fan; Finale Doshi-Velez; Luke Miratrix (Stat. Anal. Data Min., 2019)

Ensembles of Locally Independent Prediction Models. - Andrew Slavin Ross; Weiwei Pan; Leo A. Celi; Finale Doshi-Velez (AAAI, 2020)

Regional Tree Regularization for Interpretability in Deep Neural Networks. - Mike Wu; Sonali Parbhoo; Michael C. Hughes; Ryan Kindle; Leo A. Celi; Maurizio Zazzi; Volker Roth 001; Finale Doshi-Velez (AAAI, 2020)

POPCORN: Partially Observed Prediction Constrained Reinforcement Learning. - Joseph Futoma; Michael C. Hughes; Finale Doshi-Velez (AISTATS, 2020)

Prediction Focused Topic Models via Feature Selection. - Jason Ren; Russell Kunes; Finale Doshi-Velez (AISTATS, 2020)

Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients. - Mingyu Lu; Zachary Shahn; Daby Sow; Finale Doshi-Velez; Li-Wei H. Lehman (AMIA, 2020)

Defining admissible rewards for high-confidence policy evaluation in batch reinforcement learning. - Niranjani Prasad; Barbara E. Engelhardt; Finale Doshi-Velez (CHIL, 2020)

Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. - Omer Gottesman; Joseph Futoma; Yao Liu 009; Sonali Parbhoo; Leo A. Celi; Emma Brunskill; Finale Doshi-Velez (ICML, 2020)

Active Screening on Recurrent Diseases Contact Networks with Uncertainty: A Reinforcement Learning Approach. - Han-Ching Ou; Kai Wang 040; Finale Doshi-Velez; Milind Tambe (MABS, 2020)

Transfer Learning from Well-Curated to Less-Resourced Populations with HIV. - Sonali Parbhoo; Mario Wieser; Volker Roth 001; Finale Doshi-Velez (MLHC, 2020)

Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs. - Jianzhun Du; Joseph Futoma; Finale Doshi-Velez (NeurIPS, 2020)

Incorporating Interpretable Output Constraints in Bayesian Neural Networks. - Wanqian Yang; Lars Lorch; Moritz A. Graule; Himabindu Lakkaraju; Finale Doshi-Velez (NeurIPS, 2020)

PoRB-Nets: Poisson Process Radial Basis Function Networks. - Beau Coker; Melanie Fernandes Pradier; Finale Doshi-Velez (UAI, 2020)

Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning. - Joseph Futoma; Muhammad A. Masood; Finale Doshi-Velez (CoRR, 2020)

Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Sepsis Treatment. - Mingyu Lu; Zachary Shahn; Daby Sow; Finale Doshi-Velez; Li-Wei H. Lehman (CoRR, 2020)

PAC Bounds for Imitation and Model-based Batch Learning of Contextual Markov Decision Processes. - Yash Nair; Finale Doshi-Velez (CoRR, 2020)

Learned Uncertainty-Aware (LUNA) Bases for Bayesian Regression using Multi-Headed Auxiliary Networks. - Sujay Thakur; Cooper Lorsung; Yaniv Yacoby; Finale Doshi-Velez; Weiwei Pan (CoRR, 2020)

BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty. - Théo Guénais; Dimitris Vamvourellis; Yaniv Yacoby; Finale Doshi-Velez; Weiwei Pan (CoRR, 2020)

Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks. - Yaniv Yacoby; Weiwei Pan; Finale Doshi-Velez (CoRR, 2020)

Learning Interpretable Concept-Based Models with Human Feedback. - Isaac Lage; Finale Doshi-Velez (CoRR, 2020)

Artificial Intelligence & Cooperation. - Elisa Bertino; Finale Doshi-Velez; Maria L. Gini; Daniel Lopresti; David C. Parkes (CoRR, 2020)

Learning Predictive and Interpretable Timeseries Summaries from ICU Data. - Nari Johnson; Sonali Parbhoo; Andrew Slavin Ross; Finale Doshi-Velez (AMIA, 2021)

Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens. - Maia L. Jacobs; Jeffrey He; Melanie F. Pradier; Barbara D. Lam; Andrew C. Ahn; Thomas H. McCoy; Roy H. Perlis; Finale Doshi-Velez; Krzysztof Z. Gajos (CHI, 2021)

Evaluating the Interpretability of Generative Models by Interactive Reconstruction. - Andrew Slavin Ross; Nina Chen; Elisa Zhao Hang; Elena L. Glassman; Finale Doshi-Velez (CHI, 2021)

Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement. - Andrew Slavin Ross; Finale Doshi-Velez (ICML, 2021)

State Relevance for Off-Policy Evaluation. - Simon P. Shen; Yecheng Jason Ma; Omer Gottesman; Finale Doshi-Velez (ICML, 2021)

Power Constrained Bandits. - Jiayu Yao; Emma Brunskill; Weiwei Pan; Susan A. Murphy; Finale Doshi-Velez (MLHC, 2021)

Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning. - Kai Wang 040; Sanket Shah; Haipeng Chen 001; Andrew Perrault; Finale Doshi-Velez; Milind Tambe (NeurIPS, 2021)

Interactive Cohort Analysis and Hypothesis Discovery by Exploring Temporal Patterns in Population-Level Health Records. - Tianyi Zhang 001; Thomas H. McCoy; Roy H. Perlis; Finale Doshi-Velez; Elena L. Glassman (VAHC, 2021)

Machine Learning Techniques for Accountability. - Been Kim; Finale Doshi-Velez (AI Mag., 2021)

Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition. - Lifeng Jin; Lane Schwartz; Finale Doshi-Velez; Timothy Miller 002; William Schuler (Comput. Linguistics, 2021)

Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment. - Kristine Zhang; Yuanheng Wang; Jianzhun Du; Brian Chu; Leo Anthony Celi; Ryan Kindle; Finale Doshi-Velez (CoRR, 2021)

Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible. - Melanie F. Pradier; Javier Zazo; Sonali Parbhoo; Roy H. Perlis; Maurizio Zazzi; Finale Doshi-Velez (CoRR, 2021)

Learning Under Adversarial and Interventional Shifts. - Harvineet Singh; Shalmali Joshi; Finale Doshi-Velez; Himabindu Lakkaraju (CoRR, 2021)

Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning. - Kai Wang 040; Sanket Shah; Haipeng Chen 001; Andrew Perrault; Finale Doshi-Velez; Milind Tambe (CoRR, 2021)

Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data. - Beau Coker; Weiwei Pan; Finale Doshi-Velez (CoRR, 2021)

Promises and Pitfalls of Black-Box Concept Learning Models. - Anita Mahinpei; Justin Clark; Isaac Lage; Finale Doshi-Velez; Weiwei Pan (CoRR, 2021)

Online structural kernel selection for mobile health. - Eura Shin; Pedja Klasnja; Susan A. Murphy; Finale Doshi-Velez (CoRR, 2021)

Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty. - Shalmali Joshi; Sonali Parbhoo; Finale Doshi-Velez (CoRR, 2021)

Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning. - Sarah Rathnam; Susan A. Murphy; Finale Doshi-Velez (CoRR, 2021)

On Learning Prediction-Focused Mixtures. - Abhishek Sharma; Catherine Zeng; Sanjana Narayanan; Sonali Parbhoo; Finale Doshi-Velez (CoRR, 2021)

Optimizing for Interpretability in Deep Neural Networks with Tree Regularization. - Mike Wu; Sonali Parbhoo; Michael C. Hughes; Volker Roth 001; Finale Doshi-Velez (J. Artif. Intell. Res., 2021)

A Joint Learning Approach for Semi-supervised Neural Topic Modeling. - Jeffrey Chiu; Rajat Mittal; Neehal Tumma; Abhishek Sharma; Finale Doshi-Velez (SPNLP@ACL, 2022)

Towards Robust Off-Policy Evaluation via Human Inputs. - Harvineet Singh; Shalmali Joshi; Finale Doshi-Velez; Himabindu Lakkaraju (AIES, 2022)

Wide Mean-Field Bayesian Neural Networks Ignore the Data. - Beau Coker; Wessel P. Bruinsma; David R. Burt; Weiwei Pan; Finale Doshi-Velez (AISTATS, 2022)

Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation. - Ramtin Keramati; Omer Gottesman; Leo Anthony Celi; Finale Doshi-Velez; Emma Brunskill (CHIL, 2022)

Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI. - Q. Vera Liao; Yunfeng Zhang; Ronny Luss; Finale Doshi-Velez; Amit Dhurandhar (HCOMP, 2022)

"If it didn't happen, why would I change my decision?": How Judges Respond to Counterfactual Explanations for the Public Safety Assessment. - Yaniv Yacoby; Ben Green 004; Christopher L. Griffin; Finale Doshi-Velez (HCOMP, 2022)

Learning Optimal Summaries of Clinical Time-series with Concept Bottleneck Models. - Carissa Wu; Sonali Parbhoo; Marton Havasi; Finale Doshi-Velez (MLHC, 2022)

Addressing Leakage in Concept Bottleneck Models. - Marton Havasi; Sonali Parbhoo; Finale Doshi-Velez (NeurIPS, 2022)

Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. - Shengpu Tang; Maggie Makar; Michael W. Sjoding; Finale Doshi-Velez; Jenna Wiens (NeurIPS, 2022)

Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines. - Anna L. Trella; Kelly W. Zhang; Inbal Nahum-Shani; Vivek Shetty; Finale Doshi-Velez; Susan A. Murphy (Algorithms, 2022)

Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making. - Sonali Parbhoo; Shalmali Joshi; Finale Doshi-Velez (CoRR, 2022)

Policy Optimization with Sparse Global Contrastive Explanations. - Jiayu Yao; Sonali Parbhoo; Weiwei Pan; Finale Doshi-Velez (CoRR, 2022)

A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes. - Kelly W. Zhang; Omer Gottesman; Finale Doshi-Velez (CoRR, 2022)

Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry. - Mark Penrod; Harrison Termotto; Varshini Reddy; Jiayu Yao; Finale Doshi-Velez; Weiwei Pan (CoRR, 2022)

Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report. - Michael L. Littman; Ifeoma Ajunwa; Guy Berger; Craig Boutilier; Morgan Currie; Finale Doshi-Velez; Gillian K. Hadfield; Michael C. Horowitz; Charles Isbell; Hiroaki Kitano; Karen Levy; Terah Lyons; et al. (CoRR, 2022)

Does the explanation satisfy your needs?: A unified view of properties of explanations. - Zixi Chen; Varshini Subhash; Marton Havasi; Weiwei Pan; Finale Doshi-Velez (CoRR, 2022)

(When) Are Contrastive Explanations of Reinforcement Learning Helpful? - Sanjana Narayanan; Isaac Lage; Finale Doshi-Velez (CoRR, 2022)

An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks. - Jiayu Yao; Yaniv Yacoby; Beau Coker; Weiwei Pan; Finale Doshi-Velez (CoRR, 2022)

Modeling Mobile Health Users as Reinforcement Learning Agents. - Eura Shin; Siddharth Swaroop; Weiwei Pan; Susan A. Murphy; Finale Doshi-Velez (CoRR, 2022)

Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables. - Yaniv Yacoby; Weiwei Pan; Finale Doshi-Velez (J. Mach. Learn. Res., 2022)

An interpretable RL framework for pre-deployment modeling in ICU hypotension management. - Kristine Zhang; Henry Wang; Jianzhun Du; Brian Chu; Aldo Arévalo; Ryan Kindle; Leo Anthony Celi; Finale Doshi-Velez (npj Digit. Medicine, 2022)

Reward Design for an Online Reinforcement Learning Algorithm Supporting Oral Self-Care. - Anna L. Trella; Kelly W. Zhang; Inbal Nahum-Shani; Vivek Shetty; Finale Doshi-Velez; Susan A. Murphy (AAAI, 2023)

Travel-time prediction using neural-network-based mixture models. - Abhishek Sharma; Jing Zhang 030; Daniel Nikovski; Finale Doshi-Velez (ANT/EDI40, 2023)

Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs. - Haotian Fu; Jiayu Yao; Omer Gottesman; Finale Doshi-Velez; George Konidaris 001 (ICLR, 2023)

The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning. - Sarah Rathnam; Sonali Parbhoo; Weiwei Pan; Susan A. Murphy; Finale Doshi-Velez (ICML, 2023)

Robust Decision-Focused Learning for Reward Transfer. - Abhishek Sharma; Sonali Parbhoo; Omer Gottesman; Finale Doshi-Velez (CoRR, 2023)

Adaptive interventions for both accuracy and time in AI-assisted human decision making. - Siddharth Swaroop; Zana Buçinca; Finale Doshi-Velez (CoRR, 2023)

Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities. - Xudong Shen; Hannah Brown; Jiashu Tao; Martin Strobel 001; Yao Tong; Akshay Narayan 002; Harold Soh; Finale Doshi-Velez (CoRR, 2023)

Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning. - Lars Lien Ankile; Brian S. Ham; Kevin Mao; Eura Shin; Siddharth Swaroop; Finale Doshi-Velez; Weiwei Pan (CoRR, 2023)

SAP-sLDA: An Interpretable Interface for Exploring Unstructured Text. - Charumathi Badrinath; Weiwei Pan; Finale Doshi-Velez (CoRR, 2023)

Bayesian Inverse Transition Learning for Offline Settings. - Leo Benac; Sonali Parbhoo; Finale Doshi-Velez (CoRR, 2023)

Why do universal adversarial attacks work on large language models?: Geometry might be the answer. - Varshini Subhash; Anna Bialas; Weiwei Pan; Finale Doshi-Velez (CoRR, 2023)

Signature Activation: A Sparse Signal View for Holistic Saliency. - José Roberto Tello-Ayala; Akl C. Fahed; Weiwei Pan; Eugene V. Pomerantsev; Patrick T. Ellinor; Anthony Philippakis; Finale Doshi-Velez (CoRR, 2023)

Learning-to-defer for sequential medical decision-making under uncertainty. - Shalmali Joshi; Sonali Parbhoo; Finale Doshi-Velez (Trans. Mach. Learn. Res., 2023)

Online model selection by learning how compositional kernels evolve. - Eura Shin; Predrag Klasnja; Susan A. Murphy; Finale Doshi-Velez (Trans. Mach. Learn. Res., 2023)

Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks. - Eura Nofshin; Siddharth Swaroop; Weiwei Pan; Susan A. Murphy; Finale Doshi-Velez (AAMAS, 2024)

Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping. - Lauren H. Cooke; Harvey Klyne; Edwin Zhang; Cassidy Laidlaw; Milind Tambe; Finale Doshi-Velez (Tiny Papers @ ICLR, 2024)

XAI-Lyricist: Improving the Singability of AI-Generated Lyrics with Prosody Explanations. - Qihao Liang; Xichu Ma; Finale Doshi-Velez; Brian Lim; Ye Wang 007 (IJCAI, 2024)

Accuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressure. - Siddharth Swaroop; Zana Buçinca; Krzysztof Z. Gajos; Finale Doshi-Velez (IUI, 2024)

Decision-Focused Model-based Reinforcement Learning for Reward Transfer. - Abhishek Sharma; Sonali Parbhoo; Omer Gottesman; Finale Doshi-Velez (MLHC, 2024)

Inverse Reinforcement Learning with Multiple Planning Horizons. - Jiayu Yao; Weiwei Pan; Finale Doshi-Velez; Barbara E. Engelhardt (RLJ, 2024)

Semi-parametric Expert Bayesian Network Learning with Gaussian Processes and Horseshoe Priors. - Yidou Weng; Finale Doshi-Velez (CoRR, 2024)

Non-Stationary Latent Auto-Regressive Bandits. - Anna L. Trella; Walter Dempsey; Finale Doshi-Velez; Susan A. Murphy (CoRR, 2024)

Guarantee Regions for Local Explanations. - Marton Havasi; Sonali Parbhoo; Finale Doshi-Velez (CoRR, 2024)

Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials. - Anna L. Trella; Kelly W. Zhang; Inbal Nahum-Shani; Vivek Shetty; Iris Yan; Finale Doshi-Velez; Susan A. Murphy (CoRR, 2024)

Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders. - Yaniv Yacoby; Weiwei Pan; Finale Doshi-Velez (CoRR, 2024)

AI Procurement Checklists: Revisiting Implementation in the Age of AI Governance. - Tom Zick; Mason Kortz; David Eaves; Finale Doshi-Velez (CoRR, 2024)

A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning. - Eura Nofshin; Esther Brown; Brian Lim; Weiwei Pan; Finale Doshi-Velez (CoRR, 2024)

Towards Integrating Personal Knowledge into Test-Time Predictions. - Isaac Lage; Sonali Parbhoo; Finale Doshi-Velez (CoRR, 2024)

Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models. - Ze Yu Zhang; Arun Verma; Finale Doshi-Velez; Bryan Kian Hsiang Low (CoRR, 2024)

Effective Monitoring of Online Decision-Making Algorithms in Digital Intervention Implementation. - Anna L. Trella; Susobhan Ghosh; Erin E. Bonar; Lara N. Coughlin; Finale Doshi-Velez; Yongyi Guo; Pei-Yao Hung; Inbal Nahum-Shani; Vivek Shetty; Maureen A. Walton; Iris Yan; Kelly W. Zhang; et al. (CoRR, 2024)

Directly Optimizing Explanations for Desired Properties. - Hiwot Belay Tadesse; Alihan Hüyük; Weiwei Pan; Finale Doshi-Velez (CoRR, 2024)

Inverse Transition Learning: Learning Dynamics from Demonstrations. - Leo Benac; Abhishek Sharma; Sonali Parbhoo; Finale Doshi-Velez (CoRR, 2024)

Pruning the Path to Optimal Care: Identifying Systematically Suboptimal Medical Decision-Making with Inverse Reinforcement Learning. - Inko Bovenzi; Adi Carmel; Michael Hu; Rebecca M. Hurwitz; Fiona McBride; Leo Benac; José Roberto Tello-Ayala; Finale Doshi-Velez (CoRR, 2024)

Shaping AI's Impact on Billions of Lives. - Mariano-Florentino Cuellar; Jeff Dean; Finale Doshi-Velez; John L. Hennessy; Andy Konwinski; Sanmi Koyejo; Pelonomi Moiloa; Emma Pierson; David A. Patterson 001 (CoRR, 2024)

Diverse Concept Proposals for Concept Bottleneck Models. - Katrina Brown; Marton Havasi; Finale Doshi-Velez (CoRR, 2024)

Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning. - Sarah Rathnam; Sonali Parbhoo; Siddharth Swaroop; Weiwei Pan; Susan A. Murphy; Finale Doshi-Velez (J. Mach. Learn. Res., 2024)

Task-Relevant Feature Selection with Prediction Focused Mixture Models. - Abhishek Sharma; Catherine Zeng; Sanjana Narayanan; Sonali Parbhoo; Roy H. Perlis; Finale Doshi-Velez (Trans. Mach. Learn. Res., 2024)

Directions of Technical Innovation for Regulatable AI Systems. - Xudong Shen; Hannah Brown; Jiashu Tao; Martin Strobel 001; Yao Tong; Akshay Narayan 002; Harold Soh; Finale Doshi-Velez (Commun. ACM, 2024)

A Deployed Online Reinforcement Learning Algorithm in an Oral Health Clinical Trial. - Anna L. Trella; Kelly W. Zhang; Hinal Jajal; Inbal Nahum-Shani; Vivek Shetty; Finale Doshi-Velez; Susan A. Murphy (AAAI, 2025)

Decision-Point Guided Safe Policy Improvement. - Abhishek Sharma; Leo Benac; Sonali Parbhoo; Finale Doshi-Velez (AISTATS, 2025)

Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills. - Zana Buçinca; Siddharth Swaroop; Amanda E. Paluch; Finale Doshi-Velez; Krzysztof Z. Gajos (CHI, 2025)

Connecting Federated ADMM to Bayes. - Siddharth Swaroop; Mohammad Emtiyaz Khan; Finale Doshi-Velez (ICLR, 2025)

On the Effective Horizon of Inverse Reinforcement Learning. - Yiqing Xu; Finale Doshi-Velez; David Hsu (AAMAS, 2025)

User Studies in Human-Feature-Integration. - Yixin Li; Lucas Lefebvre; Sonali Parbhoo; Finale Doshi-Velez; Isaac Lage (IUI Workshops, 2025)

Personalising AI Assistance Based on Overreliance Rate in AI-Assisted Decision Making. - Siddharth Swaroop; Zana Buçinca; Krzysztof Z. Gajos; Finale Doshi-Velez (IUI, 2025)

Transparent Trade-offs between Properties of Explanations. - Hiwot Belay Tadesse; Alihan Hüyük; Yaniv Yacoby; Weiwei Pan; Finale Doshi-Velez (UAI, 2025)

Strategically Linked Decisions in Long-Term Planning and Reinforcement Learning. - Alihan Hüyük; Finale Doshi-Velez (CoRR, 2025)

Federated ADMM from Bayesian Duality. - Thomas Möllenhoff; Siddharth Swaroop; Finale Doshi-Velez; Mohammad Emtiyaz Khan (CoRR, 2025)

Estimating Upper Extremity Fugl-Meyer Assessment Scores From Reaching Motions Using Wearable Sensors. - Yu Meng Zhou; Nihal Raman; Tommaso Proietti; James Arnold; Prabhat Pathak; David Pont-Esteban; Kristin Nuckols; Kelly Rishe; Finale Doshi-Velez; David J. Lin; Conor J. Walsh (IEEE J. Biomed. Health Informatics, 2025)