Doshi-Velez, Finale

81 publications

AAAI 2025 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

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

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

UAI 2025 Transparent Trade-Offs Between Properties of Explanations Hiwot Belay Tadesse, Alihan Hüyük, Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez

ICLRW 2025 Understanding the Relationship Between Prompts and Response Uncertainty in Large Language Models Ze Yu Zhang, Arun Verma, Finale Doshi-Velez, Bryan Kian Hsiang Low

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

ICMLW 2024 AMBER: An Entropy Maximizing Environment Design Algorithm for Inverse Reinforcement Learning Paul Nitschke, Lars Lien Ankile, Eura Nofshin, Siddharth Swaroop, Finale Doshi-Velez, Weiwei Pan

NeurIPSW 2024 Accuracy Isn’t Everything: Understanding the Desiderata of AI Tools in Legal-Financial Settings Sudhan Chitgopkar, Noah Dohrmann, Stephanie Monson, Jimmy Mendez, Finale Doshi-Velez, Weiwei Pan

MLHC 2024 Decision-Focused Model-Based Reinforcement Learning for Reward Transfer Abhishek Sharma, Sonali Parbhoo, Omer Gottesman, Finale Doshi-Velez

JMLR 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

TMLR 2024 Task-Relevant Feature Selection with Prediction Focused Mixture Models Abhishek Sharma, Catherine Zeng, Sanjana Narayanan, Sonali Parbhoo, Roy H. Perlis, Finale Doshi-Velez

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

ICMLW 2023 Bayesian Inverse Transition Learning for Offline Settings Leo Benac, Sonali Parbhoo, Finale Doshi-Velez

ICMLW 2023 Discovering User Types: Characterization of User Traits by Task-Specific Behaviors in Reinforcement Learning Lars Lien Ankile, Brian Ham, Kevin Mao, Eura Shin, Siddharth Swaroop, Finale Doshi-Velez, Weiwei Pan

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

ICMLW 2023 Implications of Gaussian Process Kernel Mismatch for Out-of-Distribution Data Beau Coker, Finale Doshi-Velez

NeurIPSW 2023 Inverse Reinforcement Learning with Multiple Planning Horizons Jiayu Yao, Finale Doshi-Velez, Barbara Engelhardt

TMLR 2023 Learning-to-Defer for Sequential Medical Decision-Making Under Uncertainty Shalmali Joshi, Sonali Parbhoo, Finale Doshi-Velez

TMLR 2023 Online Model Selection by Learning How Compositional Kernels Evolve Eura Shin, Predrag Klasnja, Susan Murphy, Finale Doshi-Velez

ICLR 2023 Performance Bounds for Model and Policy Transfer in Hidden-Parameter MDPs Haotian Fu, Jiayu Yao, Omer Gottesman, Finale Doshi-Velez, George Konidaris

AAAI 2023 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

ICMLW 2023 Signature Activation: A Sparse Signal View for Holistic Saliency Jose Roberto Tello Ayala, Akl C. Fahed, Weiwei Pan, Eugene V. Pomerantsev, Patrick Thomas Ellinor, Anthony Philippakis, Finale Doshi-Velez

ICMLW 2023 Soft Prompting Might Be a Bug, Not a Feature Luke Bailey, Gustaf Ahdritz, Anat Kleiman, Siddharth Swaroop, Finale Doshi-Velez, Weiwei Pan

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

ICMLW 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

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

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

NeurIPSW 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

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

NeurIPSW 2022 Identifying Structure in the MIMIC ICU Dataset Zad Chin, Shivam Raval, Finale Doshi-Velez, Martin Wattenberg, Leo Anthony Celi

MLHC 2022 Learning Optimal Summaries of Clinical Time-Series with Concept Bottleneck Models Carissa Wu, Sonali Parbhoo, Marton Havasi, Finale Doshi-Velez

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

ICMLW 2022 Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare Shengpu Tang, Maggie Makar, Michael Sjoding, Finale Doshi-Velez, Jenna Wiens

JMLR 2022 Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez

NeurIPSW 2022 What Makes a Good Explanation?: A Harmonized View of Properties of Explanations Varshini Subhash, Zixi Chen, Marton Havasi, Weiwei Pan, Finale Doshi-Velez

NeurIPSW 2022 What Makes a Good Explanation?: A Harmonized View of Properties of Explanations Zixi Chen, Varshini Subhash, Marton Havasi, Weiwei Pan, Finale Doshi-Velez

ICML 2021 Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement Andrew Ross, Finale Doshi-Velez

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

JAIR 2021 Optimizing for Interpretability in Deep Neural Networks with Tree Regularization Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez

MLHC 2021 Power Constrained Bandits Jiayu Yao, Emma Brunskill, Weiwei Pan, Susan Murphy, Finale Doshi-Velez

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

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

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

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

NeurIPS 2020 Model-Based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs Jianzhun Du, Joseph Futoma, Finale Doshi-Velez

AISTATS 2020 POPCORN: Partially Observed Prediction Constrained Reinforcement Learning Joseph Futoma, Michael Hughes, Finale Doshi-Velez

UAI 2020 PoRB-Nets: Poisson Process Radial Basis Function Networks Beau Coker, Melanie Fernandez Pradier, Finale Doshi-Velez

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

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

JMLR 2019 A Particle-Based Variational Approach to Bayesian Non-Negative Matrix Factorization Muhammad A Masood, Finale Doshi-Velez

ICML 2019 Combining Parametric and Nonparametric Models for Off-Policy Evaluation Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, 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 Exploring Computational User Models for Agent Policy Summarization Isaac Lage, Daphna Lifschitz, Finale Doshi-Velez, Ofra Amir

JMLR 2019 Model Selection in Bayesian Neural Networks via Horseshoe Priors Soumya Ghosh, Jiayu Yao, Finale Doshi-Velez

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

ICML 2018 Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-Sensitive Learning Stefan Depeweg, Jose-Miguel Hernandez-Lobato, Finale Doshi-Velez, Steffen Udluft

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

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

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

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

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

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

JMLR 2017 A Bayesian Framework for Learning Rule Sets for Interpretable Classification Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille

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

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

NeurIPS 2017 Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes Taylor W Killian, Samuel Daulton, George Konidaris, Finale Doshi-Velez

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

JMLR 2016 Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders Huseyin Melih Elibol, Vincent Nguyen, Scott Linderman, Matthew Johnson, Amna Hashmi, Finale Doshi-Velez

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

AISTATS 2016 Spectral M-Estimation with Applications to Hidden Markov Models Dustin Tran, Minjae Kim, Finale Doshi-Velez

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

NeurIPS 2015 Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction Been Kim, Julie A Shah, 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 Approaches for Reinforcement Learning in Partially Observable Domains Finale Doshi-Velez

NeurIPS 2010 Nonparametric Bayesian Policy Priors for Reinforcement Learning Finale Doshi-velez, David Wingate, Nicholas Roy, Joshua B. Tenenbaum

ICML 2009 Accelerated Sampling for the Indian Buffet Process Finale Doshi-Velez, Zoubin Ghahramani

UAI 2009 Correlated Non-Parametric Latent Feature Models Finale Doshi-Velez, Zoubin Ghahramani

NeurIPS 2009 Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process Finale Doshi-velez, Shakir Mohamed, Zoubin Ghahramani, David A. Knowles

NeurIPS 2009 The Infinite Partially Observable Markov Decision Process Finale Doshi-velez