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ORCIDFinale Doshi-Velez
Finale Doshi
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- affiliation: Harvard University, USA
- affiliation (former): MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, USA
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2020 – today
- 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 - [i105]Abhishek Sharma, Sonali Parbhoo, Omer Gottesman, Finale Doshi-Velez:
Robust Decision-Focused Learning for Reward Transfer. CoRR abs/2304.03365 (2023) - [i104]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) - [i103]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) - [i102]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) - [i101]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) - [i100]Yiqing Xu, Finale Doshi-Velez, David Hsu:
On the Effective Horizon of Inverse Reinforcement Learning. CoRR abs/2307.06541 (2023) - [i99]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) - [i98]Charumathi Badrinath, Weiwei Pan, Finale Doshi-Velez:
SAP-sLDA: An Interpretable Interface for Exploring Unstructured Text. CoRR abs/2308.01420 (2023) - [i97]Leo Benac, Sonali Parbhoo, Finale Doshi-Velez:
Bayesian Inverse Transition Learning for Offline Settings. CoRR abs/2308.05075 (2023) - [i96]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) - [i95]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) - 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 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 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 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 - [c63]Jason Ren, Russell Kunes, Finale Doshi-Velez:
Prediction Focused Topic Models via Feature Selection. AISTATS 2020: 4420-4429 - [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 Parkes:
Artificial Intelligence & Cooperation. CoRR abs/2012.06034 (2020)
2010 – 2019
- 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]