| 2026 | ACL | Prompting the Unknown: Understanding Response Uncertainty in Large Language Models. | Ze Yu Zhang, Arun Verma, Finale Doshi-Velez, Bryan Kian Hsiang Low |
| 2026 | HCI | Person-Specific Blood Pressure Spike Modeling Using Wearable Data Across Multiple Temporal Aggregation Scales. | Ali Kargarandehkordi, Agnik Banerjee, Aditi Jaiswal, Yang Qian, Christopher R. Slade, Yinan Sun, Andrew Flynn, Mehreen Hai, Rujul Singh, Nhung Nguyen, Xuhai Xu, Kristina T. Phillips, Roberto M. Benzo, Adrian Aguilera, Finale Doshi-Velez, Peter Washington |
| 2025 | AAAI | 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 |
| 2025 | AISTATS | Decision-Point Guided Safe Policy Improvement. | Abhishek Sharma, Leo Benac, Sonali Parbhoo, Finale Doshi-Velez |
| 2025 | CHI | Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills. | Zana Buinca, Siddharth Swaroop, Amanda E. Paluch, Finale Doshi-Velez, Krzysztof Z. Gajos |
| 2025 | ICLR | Connecting Federated ADMM to Bayes. | Siddharth Swaroop, Mohammad Emtiyaz Khan, Finale Doshi-Velez |
| 2025 | IUI | User Studies in Human-Feature-Integration. | Yixin Li, Lucas Lefebvre, Sonali Parbhoo, Finale Doshi-Velez, Isaac Lage |
| 2025 | IUI | Personalising AI Assistance Based on Overreliance Rate in AI-Assisted Decision Making. | Siddharth Swaroop, Zana Buinca, Krzysztof Z. Gajos, Finale Doshi-Velez |
| 2025 | UAI | Transparent Trade-offs between Properties of Explanations. | Hiwot Belay Tadesse, Alihan Hyk, Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez |
| 2024 | ICLR | Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping. | Lauren H. Cooke, Harvey Klyne, Edwin Zhang, Cassidy Laidlaw, Milind Tambe, Finale Doshi-Velez |
| 2024 | IJCAI | XAI-Lyricist: Improving the Singability of AI-Generated Lyrics with Prosody Explanations. | Qihao Liang, Xichu Ma, Finale Doshi-Velez, Brian Lim, Ye Wang |
| 2024 | IUI | Accuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressure. | Siddharth Swaroop, Zana Buinca, Krzysztof Z. Gajos, Finale Doshi-Velez |
| 2023 | AAAI | 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 |
| 2023 | ANT | Travel-time prediction using neural-network-based mixture models. | Abhishek Sharma, Jing Zhang, Daniel Nikovski, Finale Doshi-Velez |
| 2023 | ICLR | Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs. | Haotian Fu, Jiayu Yao, Omer Gottesman, Finale Doshi-Velez, George Konidaris |
| 2023 | ICML | 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 |
| 2022 | AIES | Towards Robust Off-Policy Evaluation via Human Inputs. | Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju |
| 2022 | AISTATS | Wide Mean-Field Bayesian Neural Networks Ignore the Data. | Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez |
| 2022 | HCOMP | 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 |
| 2022 | HCOMP | "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, Christopher L. Griffin, Finale Doshi-Velez |
| 2021 | AMIA | Learning Predictive and Interpretable Timeseries Summaries from ICU Data. | Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross, Finale Doshi-Velez |
| 2021 | CHI | 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 |
| 2021 | CHI | Evaluating the Interpretability of Generative Models by Interactive Reconstruction. | Andrew Slavin Ross, Nina Chen, Elisa Zhao Hang, Elena L. Glassman, Finale Doshi-Velez |
| 2021 | ICML | Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement. | Andrew Slavin Ross, Finale Doshi-Velez |
| 2021 | ICML | State Relevance for Off-Policy Evaluation. | Simon P. Shen, Yecheng Jason Ma, Omer Gottesman, Finale Doshi-Velez |
| 2020 | AAAI | Ensembles of Locally Independent Prediction Models. | Andrew Slavin Ross, Weiwei Pan, Leo A. Celi, Finale Doshi-Velez |
| 2020 | AAAI | 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, Finale Doshi-Velez |
| 2020 | AISTATS | POPCORN: Partially Observed Prediction Constrained Reinforcement Learning. | Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez |
| 2020 | AISTATS | Prediction Focused Topic Models via Feature Selection. | Jason Ren, Russell Kunes, Finale Doshi-Velez |
| 2020 | AMIA | 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 |
| 2020 | ICML | Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. | Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo A. Celi, Emma Brunskill, Finale Doshi-Velez |
| 2020 | MABS | Active Screening on Recurrent Diseases Contact Networks with Uncertainty: A Reinforcement Learning Approach. | Han-Ching Ou, Kai Wang, Finale Doshi-Velez, Milind Tambe |
| 2020 | UAI | PoRB-Nets: Poisson Process Radial Basis Function Networks. | Beau Coker, Melanie Fernandes Pradier, Finale Doshi-Velez |
| 2019 | ACL | Unsupervised Learning of PCFGs with Normalizing Flow. | Lifeng Jin, Finale Doshi-Velez, Timothy Miller, Lane Schwartz, William Schuler |
| 2019 | HCOMP | Human Evaluation of Models Built for Interpretability. | Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Samuel J. Gershman, Finale Doshi-Velez |
| 2019 | ICML | Combining parametric and nonparametric models for off-policy evaluation. | Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez |
| 2019 | IJCAI | Exploring Computational User Models for Agent Policy Summarization. | Isaac Lage, Daphna Lifschitz, Finale Doshi-Velez, Ofra Amir |
| 2019 | IJCAI | Truly Batch Apprenticeship Learning with Deep Successor Features. | Donghun Lee, Srivatsan Srinivasan, Finale Doshi-Velez |
| 2019 | IJCAI | Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies. | Muhammad A. Masood, Finale Doshi-Velez |
| 2018 | AAAI | Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients. | Andrew Slavin Ross, Finale Doshi-Velez |
| 2018 | AAAI | Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. | Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez |
| 2018 | AISTATS | Weighted Tensor Decomposition for Learning Latent Variables with Partial Data. | Omer Gottesman, Weiwei Pan, Finale Doshi-Velez |
| 2018 | AISTATS | 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 |
| 2018 | AMIA | 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 |
| 2018 | EMNLP | Depth-bounding is effective: Improvements and Evaluation of Unsupervised PCFG Induction. | Lifeng Jin, Finale Doshi-Velez, Timothy Miller, William Schuler, Lane Schwartz |
| 2018 | ICML | Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning. | Stefan Depeweg, Jos Miguel Hernndez-Lobato, Finale Doshi-Velez, Steffen Udluft |
| 2018 | ICML | Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors. | Soumya Ghosh, Jiayu Yao, Finale Doshi-Velez |
| 2017 | AAAI | Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes. | Taylor W. Killian, George Dimitri Konidaris, Finale Doshi-Velez |
| 2017 | ICLR | Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. | Stefan Depeweg, Jos Miguel Hernndez-Lobato, Finale Doshi-Velez, Steffen Udluft |
| 2017 | IJCAI | Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. | Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez |
| 2016 | AISTATS | Spectral M-estimation with Applications to Hidden Markov Models. | Dustin Tran, Minjae Kim, Finale Doshi-Velez |
| 2016 | COLING | Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input. | Cory Shain, William Bryce, Lifeng Jin, Victoria Krakovna, Finale Doshi-Velez, Timothy Miller, William Schuler, Lane Schwartz |
| 2016 | ICDM | Bayesian Rule Sets for Interpretable Classification. | Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille |
| 2016 | IJCAI | Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations. | Finale Doshi-Velez, George Dimitri Konidaris |
| 2016 | SDM | Cost-Sensitive Batch Mode Active Learning: Designing Astronomical Observation by Optimizing Telescope Time and Telescope Choice. | Xide Xia, Pavlos Protopapas, Finale Doshi-Velez |
| 2015 | AAAI | Graph-Sparse LDA: A Topic Model with Structured Sparsity. | Finale Doshi-Velez, Byron C. Wallace, Ryan P. Adams |
| 2014 | AAAI | Preface. | Finale Doshi-Velez, David C. Kale, Byron C. Wallace, Jenna Wiens |
| 2014 | KDD | Unfolding physiological state: mortality modelling in intensive care units. | Marzyeh Ghassemi, Tristan Naumann, Finale Doshi-Velez, Nicole Brimmer, Rohit Joshi, Anna Rumshisky, Peter Szolovits |
| 2012 | ICRA | A Bayesian nonparametric approach to modeling battery health. | Joshua Mason Joseph, Finale Doshi-Velez, Nicholas Roy |
| 2011 | CogSci | A Comparison of Human and Agent Reinforcement Learning in Partially Observable Domains. | Finale Doshi-Velez, Zoubin Ghahramani |
| 2010 | AAAI | Nonparametric Bayesian Approaches for Reinforcement Learning in Partially Observable Domains. | Finale Doshi-Velez |
| 2010 | AAAI | A Bayesian Nonparametric Approach to Modeling Mobility Patterns. | Joshua Mason Joseph, Finale Doshi-Velez, Nicholas Roy |
| 2009 | ICML | Accelerated sampling for the Indian Buffet Process. | Finale Doshi-Velez, Zoubin Ghahramani |
| 2009 | UAI | Correlated Non-Parametric Latent Feature Models. | Finale Doshi-Velez, Zoubin Ghahramani |