default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDFinale Doshi-Velez
- > Home > Persons > Finale Doshi-Velez
Publications
- 2024
[j25]Sarah Rathnam, Sonali Parbhoo, Siddharth Swaroop, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez:
Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning. J. Mach. Learn. Res. 25: 255:1-255:48 (2024)- [j24]Abhishek Sharma, Catherine Zeng, Sanjana Narayanan, Sonali Parbhoo, Roy H. Perlis, Finale Doshi-Velez:
Task-Relevant Feature Selection with Prediction Focused Mixture Models. Trans. Mach. Learn. Res. 2024 (2024) - [c90]Eura Nofshin, Siddharth Swaroop, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez:
Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks. AAMAS 2024: 1482-1491 - [c89]Lauren H. Cooke, Harvey Klyne, Edwin Zhang, Cassidy Laidlaw, Milind Tambe, Finale Doshi-Velez:
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping. Tiny Papers @ ICLR 2024 - [c88]Siddharth Swaroop
, Zana Buçinca, Krzysztof Z. Gajos, Finale Doshi-Velez:
Accuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressure. IUI 2024: 138-154 - [i116]Eura Nofshin, Siddharth Swaroop, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez:
Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks. CoRR abs/2401.14923 (2024) - [i114]Anna L. Trella, Walter Dempsey, Finale Doshi-Velez, Susan A. Murphy:
Non-Stationary Latent Auto-Regressive Bandits. CoRR abs/2402.03110 (2024) - [i113]Marton Havasi, Sonali Parbhoo, Finale Doshi-Velez:
Guarantee Regions for Local Explanations. CoRR abs/2402.12737 (2024) - [i112]Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Iris Yan, Finale Doshi-Velez, Susan A. Murphy:
Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials. CoRR abs/2402.17003 (2024) - [i111]Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez:
Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders. CoRR abs/2403.08941 (2024) - [i110]Tom Zick, Mason Kortz, David Eaves, Finale Doshi-Velez:
AI Procurement Checklists: Revisiting Implementation in the Age of AI Governance. CoRR abs/2404.14660 (2024) - [i109]Eura Nofshin, Esther Brown, Brian Lim, Weiwei Pan, Finale Doshi-Velez:
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning. CoRR abs/2406.00116 (2024) - [i108]Isaac Lage, Sonali Parbhoo, Finale Doshi-Velez:
Towards Integrating Personal Knowledge into Test-Time Predictions. CoRR abs/2406.08636 (2024) - [i107]Ze Yu Zhang, Arun Verma, Finale Doshi-Velez, Bryan Kian Hsiang Low:
Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models. CoRR abs/2407.14845 (2024) - 2023
- [j23]Shalmali Joshi, Sonali Parbhoo, Finale Doshi-Velez:
Learning-to-defer for sequential medical decision-making under uncertainty. Trans. Mach. Learn. Res. 2023 (2023) - [j22]Eura Shin, Predrag Klasnja, Susan A. Murphy, Finale Doshi-Velez:
Online model selection by learning how compositional kernels evolve. Trans. Mach. Learn. Res. 2023 (2023) - [c87]Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A. Murphy:
Reward Design for an Online Reinforcement Learning Algorithm Supporting Oral Self-Care. AAAI 2023: 15724-15730 - [c86]Abhishek Sharma, Jing Zhang, Daniel Nikovski, Finale Doshi-Velez:
Travel-time prediction using neural-network-based mixture models. ANT/EDI40 2023: 1033-1038 - [c85]Haotian Fu, Jiayu Yao, Omer Gottesman, Finale Doshi-Velez, George Konidaris:
Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs. ICLR 2023 - [c84]Sarah Rathnam, Sonali Parbhoo, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez:
The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning. ICML 2023: 28746-28767 - [i106]Abhishek Sharma, Sonali Parbhoo, Omer Gottesman, Finale Doshi-Velez:
Robust Decision-Focused Learning for Reward Transfer. CoRR abs/2304.03365 (2023) - [i105]Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens:
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. CoRR abs/2305.01738 (2023) - [i104]Siddharth Swaroop, Zana Buçinca, Finale Doshi-Velez:
Adaptive interventions for both accuracy and time in AI-assisted human decision making. CoRR abs/2306.07458 (2023) - [i103]Sarah Rathnam, Sonali Parbhoo, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez:
The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning. CoRR abs/2306.11208 (2023) - [i102]Xudong Shen, Hannah Brown, Jiashu Tao, Martin Strobel, Yao Tong, Akshay Narayan, Harold Soh, Finale Doshi-Velez:
Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities. CoRR abs/2306.12609 (2023) - [i101]Yiqing Xu, Finale Doshi-Velez, David Hsu:
On the Effective Horizon of Inverse Reinforcement Learning. CoRR abs/2307.06541 (2023) - [i100]Lars Lien Ankile, Brian S. Ham, Kevin Mao, Eura Shin, Siddharth Swaroop, Finale Doshi-Velez, Weiwei Pan:
Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning. CoRR abs/2307.08169 (2023) - [i99]Charumathi Badrinath, Weiwei Pan, Finale Doshi-Velez:
SAP-sLDA: An Interpretable Interface for Exploring Unstructured Text. CoRR abs/2308.01420 (2023) - [i98]Leo Benac, Sonali Parbhoo, Finale Doshi-Velez:
Bayesian Inverse Transition Learning for Offline Settings. CoRR abs/2308.05075 (2023) - [i97]Varshini Subhash, Anna Bialas, Weiwei Pan, Finale Doshi-Velez:
Why do universal adversarial attacks work on large language models?: Geometry might be the answer. CoRR abs/2309.00254 (2023) - [i96]José Roberto Tello-Ayala, Akl C. Fahed, Weiwei Pan, Eugene V. Pomerantsev, Patrick T. Ellinor, Anthony Philippakis, Finale Doshi-Velez:
Signature Activation: A Sparse Signal View for Holistic Saliency. CoRR abs/2309.11443 (2023) - [i95]Lauren H. Cooke, Harvey Klyne, Edwin Zhang, Cassidy Laidlaw, Milind Tambe, Finale Doshi-Velez:
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping. CoRR abs/2312.09983 (2023) - 2022
- [j21]Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A. Murphy:
Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines. Algorithms 15(8): 255 (2022) - [j20]Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez:
Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables. J. Mach. Learn. Res. 23: 244:1-244:54 (2022) - [j19]Kristine Zhang, Henry Wang, Jianzhun Du, Brian Chu, Aldo Arévalo, Ryan Kindle, Leo Anthony Celi, Finale Doshi-Velez:
An interpretable RL framework for pre-deployment modeling in ICU hypotension management. npj Digit. Medicine 5 (2022) - [c83]Jeffrey Chiu, Rajat Mittal, Neehal Tumma, Abhishek Sharma, Finale Doshi-Velez:
A Joint Learning Approach for Semi-supervised Neural Topic Modeling. SPNLP@ACL 2022: 40-51 - [c82]Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju:
Towards Robust Off-Policy Evaluation via Human Inputs. AIES 2022: 686-699 - [c81]Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez:
Wide Mean-Field Bayesian Neural Networks Ignore the Data. AISTATS 2022: 5276-5333 - [c80]Ramtin Keramati, Omer Gottesman, Leo Anthony Celi, Finale Doshi-Velez, Emma Brunskill:
Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation. CHIL 2022: 397-410 - [c79]Q. Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, Amit Dhurandhar:
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI. HCOMP 2022: 147-159 - [c78]Yaniv Yacoby, Ben Green, Christopher L. Griffin, Finale Doshi-Velez:
"If it didn't happen, why would I change my decision?": How Judges Respond to Counterfactual Explanations for the Public Safety Assessment. HCOMP 2022: 219-230 - [c77]Carissa Wu, Sonali Parbhoo, Marton Havasi, Finale Doshi-Velez:
Learning Optimal Summaries of Clinical Time-series with Concept Bottleneck Models. MLHC 2022: 648-672 - [c76]Marton Havasi, Sonali Parbhoo, Finale Doshi-Velez:
Addressing Leakage in Concept Bottleneck Models. NeurIPS 2022 - [c75]Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens:
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. NeurIPS 2022 - [i94]Sonali Parbhoo, Shalmali Joshi, Finale Doshi-Velez:
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making. CoRR abs/2201.08262 (2022) - [i93]Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez:
Wide Mean-Field Bayesian Neural Networks Ignore the Data. CoRR abs/2202.11670 (2022) - [i92]Jeffrey Chiu, Rajat Mittal, Neehal Tumma, Abhishek Sharma, Finale Doshi-Velez:
A Joint Learning Approach for Semi-supervised Neural Topic Modeling. CoRR abs/2204.03208 (2022) - [i91]Yaniv Yacoby, Ben Green, Christopher L. Griffin, Finale Doshi-Velez:
"If it didn't happen, why would I change my decision?": How Judges Respond to Counterfactual Explanations for the Public Safety Assessment. CoRR abs/2205.05424 (2022) - [i90]Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A. Murphy:
Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines. CoRR abs/2206.03944 (2022) - [i89]Q. Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, Amit Dhurandhar:
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI. CoRR abs/2206.10847 (2022) - [i88]Jiayu Yao, Sonali Parbhoo, Weiwei Pan, Finale Doshi-Velez:
Policy Optimization with Sparse Global Contrastive Explanations. CoRR abs/2207.06269 (2022) - [i87]Kelly W. Zhang, Omer Gottesman, Finale Doshi-Velez:
A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes. CoRR abs/2208.00250 (2022) - [i86]Mark Penrod, Harrison Termotto, Varshini Reddy, Jiayu Yao, Finale Doshi-Velez, Weiwei Pan:
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry. CoRR abs/2208.01705 (2022) - [i85]Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A. Murphy:
Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care. CoRR abs/2208.07406 (2022) - [i84]Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju:
Towards Robust Off-Policy Evaluation via Human Inputs. CoRR abs/2209.08682 (2022) - [i83]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, Melanie Mitchell, Julie Shah, Steven A. Sloman, Shannon Vallor, Toby Walsh:
Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report. CoRR abs/2210.15767 (2022) - [i82]Zixi Chen, Varshini Subhash, Marton Havasi, Weiwei Pan, Finale Doshi-Velez:
Does the explanation satisfy your needs?: A unified view of properties of explanations. CoRR abs/2211.05667 (2022) - [i81]Sanjana Narayanan, Isaac Lage, Finale Doshi-Velez:
(When) Are Contrastive Explanations of Reinforcement Learning Helpful? CoRR abs/2211.07719 (2022) - [i80]Jiayu Yao, Yaniv Yacoby, Beau Coker, Weiwei Pan, Finale Doshi-Velez:
An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks. CoRR abs/2211.09184 (2022) - [i79]Eura Shin, Siddharth Swaroop, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez:
Modeling Mobile Health Users as Reinforcement Learning Agents. CoRR abs/2212.00863 (2022) - 2021
- [j18]Been Kim, Finale Doshi-Velez:
Machine Learning Techniques for Accountability. AI Mag. 42(1): 47-52 (2021) - [j17]Lifeng Jin, Lane Schwartz, Finale Doshi-Velez, Timothy A. Miller, William Schuler:
Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition. Comput. Linguistics 47(1): 181-216 (2021) - [j16]Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez:
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization. J. Artif. Intell. Res. 72: 1-37 (2021) - [c74]Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross, Finale Doshi-Velez:
Learning Predictive and Interpretable Timeseries Summaries from ICU Data. AMIA 2021 - [c73]Andrew Slavin Ross, Nina Chen, Elisa Zhao Hang, Elena L. Glassman, Finale Doshi-Velez:
Evaluating the Interpretability of Generative Models by Interactive Reconstruction. CHI 2021: 80:1-80:15 - [c72]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:
Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens. CHI 2021: 659:1-659:14 - [c71]Andrew Slavin Ross, Finale Doshi-Velez:
Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement. ICML 2021: 9084-9094 - [c70]Simon P. Shen, Yecheng Jason Ma, Omer Gottesman, Finale Doshi-Velez:
State Relevance for Off-Policy Evaluation. ICML 2021: 9537-9546 - [c69]Jiayu Yao, Emma Brunskill, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez:
Power Constrained Bandits. MLHC 2021: 209-259 - [c68]Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe:
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning. NeurIPS 2021: 8795-8806 - [c67]Tianyi Zhang, Thomas H. McCoy, Roy H. Perlis, Finale Doshi-Velez, Elena L. Glassman:
Interactive Cohort Analysis and Hypothesis Discovery by Exploring Temporal Patterns in Population-Level Health Records. VAHC 2021: 14-18 - [i78]Kristine Zhang, Yuanheng Wang, Jianzhun Du, Brian Chu, Leo Anthony Celi, Ryan Kindle, Finale Doshi-Velez:
Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment. CoRR abs/2101.03309 (2021) - [i77]Melanie F. Pradier, Javier Zazo, Sonali Parbhoo, Roy H. Perlis, Maurizio Zazzi, Finale Doshi-Velez:
Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible. CoRR abs/2101.05360 (2021) - [i76]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:
Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens. CoRR abs/2102.00593 (2021) - [i75]Andrew Slavin Ross, Nina Chen, Elisa Zhao Hang, Elena L. Glassman, Finale Doshi-Velez:
Evaluating the Interpretability of Generative Models by Interactive Reconstruction. CoRR abs/2102.01264 (2021) - [i74]Andrew Slavin Ross, Finale Doshi-Velez:
Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement. CoRR abs/2102.05185 (2021) - [i73]Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju:
Learning Under Adversarial and Interventional Shifts. CoRR abs/2103.15933 (2021) - [i72]Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe:
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning. CoRR abs/2106.03279 (2021) - [i71]Beau Coker, Weiwei Pan, Finale Doshi-Velez:
Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data. CoRR abs/2106.07052 (2021) - [i70]Anita Mahinpei, Justin Clark, Isaac Lage, Finale Doshi-Velez, Weiwei Pan:
Promises and Pitfalls of Black-Box Concept Learning Models. CoRR abs/2106.13314 (2021) - [i69]Eura Shin, Pedja Klasnja, Susan A. Murphy, Finale Doshi-Velez:
Online structural kernel selection for mobile health. CoRR abs/2107.09949 (2021) - [i68]Simon P. Shen, Yecheng Jason Ma, Omer Gottesman, Finale Doshi-Velez:
State Relevance for Off-Policy Evaluation. CoRR abs/2109.06310 (2021) - [i67]Shalmali Joshi, Sonali Parbhoo, Finale Doshi-Velez:
Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty. CoRR abs/2109.06312 (2021) - [i66]Sarah Rathnam, Susan A. Murphy, Finale Doshi-Velez:
Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning. CoRR abs/2109.08134 (2021) - [i65]Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross, Finale Doshi-Velez:
Learning Predictive and Interpretable Timeseries Summaries from ICU Data. CoRR abs/2109.11043 (2021) - [i64]Abhishek Sharma, Catherine Zeng, Sanjana Narayanan, Sonali Parbhoo, Finale Doshi-Velez:
On Learning Prediction-Focused Mixtures. CoRR abs/2110.13221 (2021) - [i63]Ramtin Keramati, Omer Gottesman, Leo Anthony Celi, Finale Doshi-Velez, Emma Brunskill:
Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation. CoRR abs/2111.14272 (2021) - 2020
- [c66]Andrew Slavin Ross, Weiwei Pan, Leo A. Celi, Finale Doshi-Velez:
Ensembles of Locally Independent Prediction Models. AAAI 2020: 5527-5536 - [c65]Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo A. Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez:
Regional Tree Regularization for Interpretability in Deep Neural Networks. AAAI 2020: 6413-6421 - [c64]Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez:
POPCORN: Partially Observed Prediction Constrained Reinforcement Learning. AISTATS 2020: 3578-3588 - [c62]Mingyu Lu, Zachary Shahn, Daby Sow, Finale Doshi-Velez, Li-Wei H. Lehman:
Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients. AMIA 2020 - [c61]Niranjani Prasad, Barbara E. Engelhardt, Finale Doshi-Velez:
Defining admissible rewards for high-confidence policy evaluation in batch reinforcement learning. CHIL 2020: 1-9 - [c60]Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo A. Celi, Emma Brunskill, Finale Doshi-Velez:
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. ICML 2020: 3658-3667 - [c59]Han-Ching Ou, Kai Wang, Finale Doshi-Velez, Milind Tambe:
Active Screening on Recurrent Diseases Contact Networks with Uncertainty: A Reinforcement Learning Approach. MABS 2020: 54-65 - [c58]Sonali Parbhoo, Mario Wieser, Volker Roth, Finale Doshi-Velez:
Transfer Learning from Well-Curated to Less-Resourced Populations with HIV. MLHC 2020: 589-609 - [c57]Jianzhun Du, Joseph Futoma, Finale Doshi-Velez:
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs. NeurIPS 2020 - [c56]Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju, Finale Doshi-Velez:
Incorporating Interpretable Output Constraints in Bayesian Neural Networks. NeurIPS 2020 - [c55]Beau Coker, Melanie Fernandes Pradier, Finale Doshi-Velez:
PoRB-Nets: Poisson Process Radial Basis Function Networks. UAI 2020: 1338-1347 - [e6]Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2020, 7-8 August 2020, Virtual Event, Durham, NC, USA. Proceedings of Machine Learning Research 126, PMLR 2020 [contents] - [i62]Joseph Futoma, Muhammad A. Masood, Finale Doshi-Velez:
Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning. CoRR abs/2001.03224 (2020) - [i61]Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez:
POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning. CoRR abs/2001.04032 (2020) - [i60]Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez:
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. CoRR abs/2002.03478 (2020) - [i59]Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez:
Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders. CoRR abs/2003.07756 (2020) - [i58]Jiayu Yao, Emma Brunskill, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez:
Power-Constrained Bandits. CoRR abs/2004.06230 (2020) - [i57]Mingyu Lu, Zachary Shahn, Daby Sow, Finale Doshi-Velez, Li-Wei H. Lehman:
Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Sepsis Treatment. CoRR abs/2005.04301 (2020) - [i56]Yash Nair, Finale Doshi-Velez:
PAC Bounds for Imitation and Model-based Batch Learning of Contextual Markov Decision Processes. CoRR abs/2006.06352 (2020) - [i55]Sujay Thakur, Cooper Lorsung, Yaniv Yacoby, Finale Doshi-Velez, Weiwei Pan:
Learned Uncertainty-Aware (LUNA) Bases for Bayesian Regression using Multi-Headed Auxiliary Networks. CoRR abs/2006.11695 (2020) - [i54]Jianzhun Du, Joseph Futoma, Finale Doshi-Velez:
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs. CoRR abs/2006.16210 (2020) - [i53]Théo Guénais, Dimitris Vamvourellis, Yaniv Yacoby, Finale Doshi-Velez, Weiwei Pan:
BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty. CoRR abs/2007.06096 (2020) - [i52]Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez:
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks. CoRR abs/2007.07124 (2020) - [i51]Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju, Finale Doshi-Velez:
Incorporating Interpretable Output Constraints in Bayesian Neural Networks. CoRR abs/2010.10969 (2020) - [i50]Isaac Lage, Finale Doshi-Velez:
Learning Interpretable Concept-Based Models with Human Feedback. CoRR abs/2012.02898 (2020) - [i49]Elisa Bertino, Finale Doshi-Velez, Maria L. Gini, Daniel Lopresti, David C. Parkes:
Artificial Intelligence & Cooperation. CoRR abs/2012.06034 (2020) - 2019
- [j15]Ofra Amir, Finale Doshi-Velez, David Sarne:
Summarizing agent strategies. Auton. Agents Multi Agent Syst. 33(5): 628-644 (2019) - [j14]Muhammad A. Masood, Finale Doshi-Velez:
A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization. J. Mach. Learn. Res. 20: 90:1-90:56 (2019) - [j13]Soumya Ghosh, Jiayu Yao, Finale Doshi-Velez:
Model Selection in Bayesian Neural Networks via Horseshoe Priors. J. Mach. Learn. Res. 20: 182:1-182:46 (2019) - [j12]Angela Fan, Finale Doshi-Velez, Luke Miratrix:
Assessing topic model relevance: Evaluation and informative priors. Stat. Anal. Data Min. 12(3): 210-222 (2019) - [c54]Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez:
Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders. AABI 2019: 1-17 - [c53]Lifeng Jin, Finale Doshi-Velez, Timothy A. Miller, Lane Schwartz, William Schuler:
Unsupervised Learning of PCFGs with Normalizing Flow. ACL (1) 2019: 2442-2452 - [c52]Isaac Lage, Daphna Lifschitz, Finale Doshi-Velez, Ofra Amir:
Toward Robust Policy Summarization. AAMAS 2019: 2081-2083 - [c51]Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Samuel J. Gershman, Finale Doshi-Velez:
Human Evaluation of Models Built for Interpretability. HCOMP 2019: 59-67 - [c50]Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez:
Combining parametric and nonparametric models for off-policy evaluation. ICML 2019: 2366-2375 - [c49]Isaac Lage, Daphna Lifschitz, Finale Doshi-Velez, Ofra Amir:
Exploring Computational User Models for Agent Policy Summarization. IJCAI 2019: 1401-1407 - [c48]Donghun Lee, Srivatsan Srinivasan, Finale Doshi-Velez:
Truly Batch Apprenticeship Learning with Deep Successor Features. IJCAI 2019: 5909-5915 - [c47]Muhammad A. Masood, Finale Doshi-Velez:
Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies. IJCAI 2019: 5923-5929 - [e5]Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2019, 9-10 August 2019, Ann Arbor, Michigan, USA. Proceedings of Machine Learning Research 106, PMLR 2019 [contents] - [i48]Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li-Wei H. Lehman, Andrew Slavin Ross, Aldo Faisal, Finale Doshi-Velez:
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. CoRR abs/1901.04670 (2019) - [i47]Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Sam Gershman, Finale Doshi-Velez:
An Evaluation of the Human-Interpretability of Explanation. CoRR abs/1902.00006 (2019) - [i46]Donghun Lee, Srivatsan Srinivasan, Finale Doshi-Velez:
Truly Batch Apprenticeship Learning with Deep Successor Features. CoRR abs/1903.10077 (2019) - [i45]Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez:
Combining Parametric and Nonparametric Models for Off-Policy Evaluation. CoRR abs/1905.05787 (2019) - [i44]Wanqian Yang, Lars Lorch, Moritz A. Graule, Srivatsan Srinivasan, Anirudh Suresh, Jiayu Yao, Melanie F. Pradier, Finale Doshi-Velez:
Output-Constrained Bayesian Neural Networks. CoRR abs/1905.06287 (2019) - [i43]Omer Gottesman, Weiwei Pan, Finale Doshi-Velez:
A general method for regularizing tensor decomposition methods via pseudo-data. CoRR abs/1905.10424 (2019) - [i42]Niranjani Prasad, Barbara E. Engelhardt, Finale Doshi-Velez:
Defining Admissible Rewards for High Confidence Policy Evaluation. CoRR abs/1905.13167 (2019) - [i41]Isaac Lage, Daphna Lifschitz, Finale Doshi-Velez, Ofra Amir:
Exploring Computational User Models for Agent Policy Summarization. CoRR abs/1905.13271 (2019) - [i40]Muhammad A. Masood, Finale Doshi-Velez:
Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies. CoRR abs/1906.00088 (2019) - [i39]Jiayu Yao, Weiwei Pan, Soumya Ghosh, Finale Doshi-Velez:
Quality of Uncertainty Quantification for Bayesian Neural Network Inference. CoRR abs/1906.09686 (2019) - [i38]Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo A. Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez:
Regional Tree Regularization for Interpretability in Black Box Models. CoRR abs/1908.04494 (2019) - [i37]Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez:
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization. CoRR abs/1908.05254 (2019) - [i35]Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez:
Learning Deep Bayesian Latent Variable Regression Models that Generalize: When Non-identifiability is a Problem. CoRR abs/1911.00569 (2019) - [i34]Andrew Slavin Ross, Weiwei Pan, Leo Anthony Celi, Finale Doshi-Velez:
Ensembles of Locally Independent Prediction Models. CoRR abs/1911.01291 (2019) - [i32]Beau Coker, Melanie F. Pradier, Finale Doshi-Velez:
Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks. CoRR abs/1912.05779 (2019) - 2018
- [j11]Lifeng Jin, Finale Doshi-Velez, Timothy A. Miller, William Schuler, Lane Schwartz:
Unsupervised Grammar Induction with Depth-bounded PCFG. Trans. Assoc. Comput. Linguistics 6: 211-224 (2018) - [j10]Michael Glueck, Mahdi Pakdaman Naeini, Finale Doshi-Velez, Fanny Chevalier, Azam Khan, Daniel Wigdor, Michael Brudno:
PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models. IEEE Trans. Vis. Comput. Graph. 24(1): 371-381 (2018) - [c46]Andrew Slavin Ross, Finale Doshi-Velez:
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients. AAAI 2018: 1660-1669 - [c45]Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez:
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. AAAI 2018: 1670-1678 - [c44]Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez:
Semi-Supervised Prediction-Constrained Topic Models. AISTATS 2018: 1067-1076 - [c43]Omer Gottesman, Weiwei Pan, Finale Doshi-Velez:
Weighted Tensor Decomposition for Learning Latent Variables with Partial Data. AISTATS 2018: 1664-1672 - [c42]Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li-Wei H. Lehman, Andrew Slavin Ross, Aldo Faisal, Finale Doshi-Velez:
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. AMIA 2018 - [c41]Ofra Amir, Finale Doshi-Velez, David Sarne:
Agent Strategy Summarization. AAMAS 2018: 1203-1207 - [c40]Lifeng Jin, Finale Doshi-Velez, Timothy A. Miller, William Schuler, Lane Schwartz:
Depth-bounding is effective: Improvements and Evaluation of Unsupervised PCFG Induction. EMNLP 2018: 2721-2731 - [c39]Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft:
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning. ICML 2018: 1192-1201 - [c38]Soumya Ghosh, Jiayu Yao, Finale Doshi-Velez:
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors. ICML 2018: 1739-1748 - [c37]Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo A. Faisal, Finale Doshi-Velez, Emma Brunskill:
Representation Balancing MDPs for Off-policy Policy Evaluation. NeurIPS 2018: 2649-2658 - [c36]Isaac Lage, Andrew Slavin Ross, Samuel J. Gershman, Been Kim, Finale Doshi-Velez:
Human-in-the-Loop Interpretability Prior. NeurIPS 2018: 10180-10189 - [e4]Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2018, 17-18 August 2018, Palo Alto, California. Proceedings of Machine Learning Research 85, PMLR 2018 [contents] - [i31]Menaka Narayanan, Emily Chen, Jeffrey He, Been Kim, Sam Gershman, Finale Doshi-Velez:
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation. CoRR abs/1802.00682 (2018) - [i30]Lifeng Jin, Finale Doshi-Velez, Timothy A. Miller, William Schuler, Lane Schwartz:
Unsupervised Grammar Induction with Depth-bounded PCFG. CoRR abs/1802.08545 (2018) - [i29]Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, Emma Brunskill:
Representation Balancing MDPs for Off-Policy Policy Evaluation. CoRR abs/1805.09044 (2018) - [i28]Isaac Lage, Andrew Slavin Ross, Been Kim, Samuel J. Gershman, Finale Doshi-Velez:
Human-in-the-Loop Interpretability Prior. CoRR abs/1805.11571 (2018) - [i27]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, Christopher Mosch, Li-Wei H. Lehman, Matthieu Komorowski, Aldo Faisal, Leo Anthony Celi, David A. Sontag, Finale Doshi-Velez:
Evaluating Reinforcement Learning Algorithms in Observational Health Settings. CoRR abs/1805.12298 (2018) - [i26]Soumya Ghosh, Jiayu Yao, Finale Doshi-Velez:
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors. CoRR abs/1806.05975 (2018) - [i25]Andrew Slavin Ross, Weiwei Pan, Finale Doshi-Velez:
Learning Qualitatively Diverse and Interpretable Rules for Classification. CoRR abs/1806.08716 (2018) - [i24]Aniruddh Raghu, Omer Gottesman, Yao Liu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, Emma Brunskill:
Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters. CoRR abs/1807.01066 (2018) - [i23]Lifeng Jin, Finale Doshi-Velez, Timothy A. Miller, William Schuler, Lane Schwartz:
Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction. CoRR abs/1809.03112 (2018) - [i22]Melanie F. Pradier, Weiwei Pan, Jiayu Yao, Soumya Ghosh, Finale Doshi-Velez:
Latent Projection BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights. CoRR abs/1811.07006 (2018) - 2017
- [j9]Mike Wu, Marzyeh Ghassemi, Mengling Feng, Leo A. Celi, Peter Szolovits, Finale Doshi-Velez:
Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database. J. Am. Medical Informatics Assoc. 24(3): 488-495 (2017) - [j8]Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille:
A Bayesian Framework for Learning Rule Sets for Interpretable Classification. J. Mach. Learn. Res. 18: 70:1-70:37 (2017) - [j7]Finale Doshi-Velez, Sinead A. Williamson:
Restricted Indian buffet processes. Stat. Comput. 27(5): 1205-1223 (2017) - [c35]Taylor W. Killian, George Dimitri Konidaris, Finale Doshi-Velez:
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes. AAAI 2017: 4949-4950 - [c34]Marzyeh Ghassemi, Mike Wu, Michael C. Hughes, Peter Szolovits, Finale Doshi-Velez:
Predicting intervention onset in the ICU with switching state space models. CRI 2017 - [c33]Sonali Parbhoo, Jasmina Bogojeska, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez:
Combining Kernel and Model Based Learning for HIV Therapy Selection. CRI 2017 - [c32]Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft:
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. ICLR (Poster) 2017 - [c31]Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez:
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. IJCAI 2017: 2662-2670 - [c30]Taylor W. Killian, Samuel Daulton, Finale Doshi-Velez, George Dimitri Konidaris:
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes. NIPS 2017: 6250-6261 - [e3]Finale Doshi-Velez, Jim Fackler, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Health Care Conference, MLHC 2017, Boston, Massachusetts, USA, 18-19 August 2017. Proceedings of Machine Learning Research 68, PMLR 2017 [contents] - [i21]Angela Fan, Finale Doshi-Velez, Luke Miratrix:
Promoting Domain-Specific Terms in Topic Models with Informative Priors. CoRR abs/1701.03227 (2017) - [i20]Finale Doshi-Velez, Been Kim:
A Roadmap for a Rigorous Science of Interpretability. CoRR abs/1702.08608 (2017) - [i19]Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez:
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. CoRR abs/1703.03717 (2017) - [i18]Taylor W. Killian, Samuel Daulton, George Dimitri Konidaris, Finale Doshi-Velez:
Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes. CoRR abs/1706.06544 (2017) - [i17]Michael C. Hughes, Leah Weiner, Gabriel Hope, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez:
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models. CoRR abs/1707.07341 (2017) - [i16]Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft:
Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems. CoRR abs/1710.07283 (2017) - [i15]Finale Doshi-Velez, Mason Kortz, Ryan Budish, Chris Bavitz, Sam Gershman, David O'Brien, Stuart Schieber, James Waldo, David Weinberger, Alexandra Wood:
Accountability of AI Under the Law: The Role of Explanation. CoRR abs/1711.01134 (2017) - [i14]Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez:
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. CoRR abs/1711.06178 (2017) - [i13]Andrew Slavin Ross, Finale Doshi-Velez:
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients. CoRR abs/1711.09404 (2017) - [i12]Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez:
Prediction-Constrained Topic Models for Antidepressant Recommendation. CoRR abs/1712.00499 (2017) - 2016
- [j6]Huseyin Melih Elibol, Vincent Nguyen, Scott W. Linderman, Matthew J. Johnson, Amna Hashmi, Finale Doshi-Velez:
Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders. J. Mach. Learn. Res. 17: 133:1-133:38 (2016) - [c29]Dustin Tran, Minjae Kim, Finale Doshi-Velez:
Spectral M-estimation with Applications to Hidden Markov Models. AISTATS 2016: 1421-1430 - [c28]Cory Shain, William Bryce, Lifeng Jin, Victoria Krakovna, Finale Doshi-Velez, Timothy A. Miller, William Schuler, Lane Schwartz:
Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input. COLING 2016: 964-975 - [c27]Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille:
Bayesian Rule Sets for Interpretable Classification. ICDM 2016: 1269-1274 - [c26]Finale Doshi-Velez, George Dimitri Konidaris:
Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations. IJCAI 2016: 1432-1440 - [c25]Xide Xia, Pavlos Protopapas, Finale Doshi-Velez:
Cost-Sensitive Batch Mode Active Learning: Designing Astronomical Observation by Optimizing Telescope Time and Telescope Choice. SDM 2016: 477-485 - [e2]Finale Doshi-Velez, Jim Fackler, David C. Kale, Byron C. Wallace, Jenna Wiens:
Proceedings of the 1st Machine Learning in Health Care, MLHC 2016, Los Angeles, CA, USA, August 19-20, 2016. JMLR Workshop and Conference Proceedings 56, JMLR.org 2016 [contents] - [i11]Dustin Tran, Minjae Kim, Finale Doshi-Velez:
Spectral M-estimation with Applications to Hidden Markov Models. CoRR abs/1603.08815 (2016) - [i10]Weiwei Pan, Finale Doshi-Velez:
A Characterization of the Non-Uniqueness of Nonnegative Matrix Factorizations. CoRR abs/1604.00653 (2016) - [i9]Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft:
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. CoRR abs/1605.07127 (2016) - [i7]Arjumand Masood, Weiwei Pan, Finale Doshi-Velez:
An Empirical Comparison of Sampling Quality Metrics: A Case Study for Bayesian Nonnegative Matrix Factorization. CoRR abs/1606.06250 (2016) - [i5]Taylor W. Killian, George Dimitri Konidaris, Finale Doshi-Velez:
Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes. CoRR abs/1612.00475 (2016) - 2015
- [j5]Finale Doshi-Velez, David Pfau, Frank D. Wood, Nicholas Roy:
Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 394-407 (2015) - [c24]Finale Doshi-Velez, Byron C. Wallace, Ryan P. Adams:
Graph-Sparse LDA: A Topic Model with Structured Sparsity. AAAI 2015: 2575-2581 - [c23]Been Kim, Julie A. Shah, Finale Doshi-Velez:
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction. NIPS 2015: 2260-2268 - [i4]Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille:
Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems. CoRR abs/1504.07614 (2015) - 2014
- [c22]Finale Doshi-Velez, David C. Kale, Byron C. Wallace, Jenna Wiens:
Preface. AAAI Workshop: Modern Artificial Intelligence for Health Analytics 2014 - [c21]George Dimitri Konidaris, Finale Doshi-Velez:
Hidden Parameter Markov Decision Processes: An Emerging Paradigm for Modeling Families of Related Tasks. AAAI Fall Symposia 2014 - [c20]Marzyeh Ghassemi, Tristan Naumann, Finale Doshi-Velez, Nicole Brimmer, Rohit Joshi, Anna Rumshisky, Peter Szolovits:
Unfolding physiological state: mortality modelling in intensive care units. KDD 2014: 75-84 - [e1]Finale Doshi-Velez, David C. Kale, Byron C. Wallace, Jenna Wiens:
Modern Artificial Intelligence for Health Analytics, Papers from the 2014 AAAI Workshop, Québec City, Québec, Canada, July 27, 2014. AAAI Technical Report WS-14-08, AAAI 2014 [contents] - [i3]Finale Doshi-Velez, Byron C. Wallace, Ryan P. Adams:
Graph-Sparse LDA: A Topic Model with Structured Sparsity. CoRR abs/1410.4510 (2014) - 2013
- [i2]Finale Doshi-Velez, George Dimitri Konidaris:
Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations. CoRR abs/1308.3513 (2013) - 2012
- [j4]Finale Doshi-Velez, Joelle Pineau, Nicholas Roy:
Reinforcement learning with limited reinforcement: Using Bayes risk for active learning in POMDPs. Artif. Intell. 187: 115-132 (2012) - [c19]Joshua Mason Joseph, Finale Doshi-Velez, Nicholas Roy:
A Bayesian nonparametric approach to modeling battery health. ICRA 2012: 1876-1882 - [i1]Finale Doshi-Velez, Zoubin Ghahramani:
Correlated Non-Parametric Latent Feature Models. CoRR abs/1205.2650 (2012) - 2011
- [j3]Mark Buller, Paul Cuddihy, Ernest Davis, Patrick Doherty, Finale Doshi-Velez, Esra Erdem, Douglas H. Fisher, Nancy L. Green, Knut Hinkelmann, Mary Lou Maher, James McLurkin, Rajiv T. Maheswaran, Sara Rubinelli, Nathan Schurr, Donia Scott, Dylan A. Shell, Pedro A. Szekely, Barbara Thönssen, Arnold B. Urken:
Reports of the AAAI 2011 Spring Symposia. AI Mag. 32(3): 119-127 (2011) - [j2]Joshua Mason Joseph, Finale Doshi-Velez, Albert S. Huang, Nicholas Roy:
A Bayesian nonparametric approach to modeling motion patterns. Auton. Robots 31(4): 383-400 (2011) - [c18]Mark Buller, Paul Cuddihy, Finale Doshi-Velez:
Organizing Committee. AAAI Spring Symposium: Computational Physiology 2011 - [c17]Finale Doshi-Velez, Zoubin Ghahramani:
A Comparison of Human and Agent Reinforcement Learning in Partially Observable Domains. CogSci 2011 - [c16]Alborz Geramifard, Finale Doshi, Josh Redding, Nicholas Roy, Jonathan P. How:
Online Discovery of Feature Dependencies. ICML 2011: 881-888 - [c15]Finale Doshi, David Wingate, Joshua B. Tenenbaum, Nicholas Roy:
Infinite Dynamic Bayesian Networks. ICML 2011: 913-920 - 2010
- [c13]Joshua Mason Joseph, Finale Doshi-Velez, Nicholas Roy:
A Bayesian Nonparametric Approach to Modeling Mobility Patterns. AAAI 2010: 1587-1593 - [c12]Finale Doshi-Velez, David Wingate, Nicholas Roy, Joshua B. Tenenbaum:
Nonparametric Bayesian Policy Priors for Reinforcement Learning. NIPS 2010: 532-540 - 2009
- [c11]Finale Doshi-Velez, Zoubin Ghahramani:
Accelerated sampling for the Indian Buffet Process. ICML 2009: 273-280 - [c9]Finale Doshi-Velez, David A. Knowles, Shakir Mohamed, Zoubin Ghahramani:
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process. NIPS 2009: 1294-1302 - [c8]Finale Doshi-Velez, Zoubin Ghahramani:
Correlated Non-Parametric Latent Feature Models. UAI 2009: 143-150 - [c7]Finale Doshi, Kurt Miller, Jurgen Van Gael, Yee Whye Teh:
Variational Inference for the Indian Buffet Process. AISTATS 2009: 137-144 - 2008
- [j1]Finale Doshi, Nicholas Roy:
Spoken language interaction with model uncertainty: an adaptive human-robot interaction system. Connect. Sci. 20(4): 299-318 (2008) - [c6]Finale Doshi, Nicholas Roy:
The permutable POMDP: fast solutions to POMDPs for preference elicitation. AAMAS (1) 2008: 493-500 - [c5]Finale Doshi, Joelle Pineau, Nicholas Roy:
Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs. ICML 2008: 256-263 - [c4]Finale Doshi, Joelle Pineau, Nicholas Roy:
Reinforcement Learning with Limited Reinforcement: Using Bayes Risk for Active Learning in POMDPs. ISAIM 2008 - 2007
- [c3]Finale Doshi, Nicholas Roy:
Efficient Model Learning for Dialog Management. AAAI Spring Symposium: Multidisciplinary Collaboration for Socially Assistive Robotics 2007: 7-14 - [c2]Finale Doshi, Nicholas Roy:
Efficient model learning for dialog management. HRI 2007: 65-72 - [c1]Finale Doshi, Emma Brunskill, Alexander C. Shkolnik, Thomas Kollar, Khashayar Rohanimanesh, Russ Tedrake, Nicholas Roy:
Collision detection in legged locomotion using supervised learning. IROS 2007: 317-322
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-09-18 01:11 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
