Finale Doshi-Velez

发表

Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables

José Miguel Hernández-Lobato, Stefan Depeweg, Finale Doshi-Velez, 2017, 1706.08495.

Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks pdf

Finale Doshi-Velez, Steffen Udluft, José Miguel Hernández-Lobato, 2016, ICLR.

Do no harm: a roadmap for responsible machine learning for health care

David C. Kale, S. Saria, A. Goldenberg, 2019, Nature Medicine.

Collision detection in legged locomotion using supervised learning

Nicholas Roy, Finale Doshi-Velez, Emma Brunskill, 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Zoubin Ghahramani, Finale Doshi-Velez, Zoubin Ghahramani, 2009, UAI.

Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process

Zoubin Ghahramani, Finale Doshi-Velez, David A. Knowles, 2009, NIPS.

Accelerated sampling for the Indian Buffet Process

Zoubin Ghahramani, Finale Doshi-Velez, Zoubin Ghahramani, 2009, ICML '09.

Variational Inference for the Indian Buffet Process

Yee Whye Teh, Finale Doshi-Velez, Kurt Miller, 2009, AISTATS.

Supervised topic models for clinical interpretability

Michael C. Hughes, Huseyin Melih Elibol, Finale Doshi-Velez, 2016, 1612.01678.

Comorbidity Clusters in Autism Spectrum Disorders: An Electronic Health Record Time-Series Analysis

Finale Doshi-Velez, I. Kohane, Y. Ge, 2014, Pediatrics.

Guidelines for reinforcement learning in healthcare

Fredrik D. Johansson, Aldo A. Faisal, Finale Doshi-Velez, 2019, Nature Medicine.

On Learning Prediction-Focused Mixtures pdf

Finale Doshi-Velez, Sonali Parbhoo, Abhishek Sharma, 2021, ArXiv.

Optimizing for Interpretability in Deep Neural Networks with Tree Regularization pdf

Mike Wu, Finale Doshi-Velez, Volker Roth, 2019, J. Artif. Intell. Res..

Nonparametric Bayesian Policy Priors for Reinforcement Learning

Joshua B. Tenenbaum, Nicholas Roy, Finale Doshi-Velez, 2010, NIPS.

Direct Policy Transfer via Hidden Parameter Markov Decision Processes

Taylor W. Killian, G. Konidaris, Finale Doshi-Velez, 2018 .

The permutable POMDP: fast solutions to POMDPs for preference elicitation

Nicholas Roy, Finale Doshi-Velez, N. Roy, 2008, AAMAS.

Efficient model learning for dialog management

Nicholas Roy, Finale Doshi-Velez, N. Roy, 2007, 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

Bayesian nonparametric approaches for reinforcement learning in partially observable domains

Nicholas Roy, Finale Doshi-Velez, N. Roy, 2012 .

Spoken language interaction with model uncertainty: an adaptive human–robot interaction system

Nicholas Roy, Finale Doshi-Velez, N. Roy, 2008, Connect. Sci..

The myth of generalisability in clinical research and machine learning in health care

Finale Doshi-Velez, L. Celi, J. Futoma, 2020, The Lancet Digital Health.

Online Discovery of Feature Dependencies

Alborz Geramifard, Jonathan P. How, Joshua D. Redding, 2011, ICML.

Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs

Joelle Pineau, Nicholas Roy, Finale Doshi-Velez, 2008, ICML '08.

The Infinite Partially Observable Markov Decision Process

Finale Doshi-Velez, Finale Doshi-Velez, F. Doshi-Velez, 2009, NIPS.

Challenges in Computing and Optimizing Upper Bounds of Marginal Likelihood based on Chi-Square Divergences

Michael C. Hughes, Finale Doshi-Velez, M. Pradier, 2019 .

Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

Andrew Slavin Ross, Finale Doshi-Velez, Michael C. Hughes, 2017, IJCAI.

Towards A Rigorous Science of Interpretable Machine Learning

Been Kim, Finale Doshi-Velez, Finale Doshi-Velez, 2017, 1702.08608.

Reports of the AAAI 2011 Spring Symposia

Nancy Green, Knut Hinkelmann, Dylan A. Shell, 2011, AI Mag..

Prediction-Constrained Topic Models for Antidepressant Recommendation pdf

Finale Doshi-Velez, Erik B. Sudderth, Michael C. Hughes, 2017, ArXiv.

Explainable Reinforcement Learning via Reward Decomposition

Alan Fern, Finale Doshi-Velez, M. Erwig, 2019 .

Improving safety and operational efficiency in residential care settings with WiFi-based localization.

N. Roy, B. Charrow, Finale Doshi-Velez, 2012, Journal of the American Medical Directors Association.

Diversity-Inducing Policy Gradient : Using MMD to find a set of policies that are diverse in terms of state-visitation

Finale Doshi-Velez, M. A. Masood, 2018 .

Rapid Posterior Exploration in Bayesian Non-negative Matrix Factorization

Finale Doshi-Velez, M. A. Masood, F. Doshi-Velez, 2016, 1610.08928.

Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights

Finale Doshi-Velez, S. Ghosh, Jiayu Yao, 2018, 1811.07006.

Projected BNNs : Avoiding weight-space pathologies by projecting neural network weights

Finale Doshi-Velez, S. Ghosh, Jiayu Yao, 2018 .

Electronic Health Record Based Algorithm to Identify Patients with Autism Spectrum Disorder

Finale Doshi-Velez, Pei Chen, Joseph Bochenek, 2016, PloS one.

Learned Uncertainty-Aware (LUNA) Bases for Bayesian Regression using Multi-Headed Auxiliary Networks pdf

Finale Doshi-Velez, Weiwei Pan, Yaniv Yacoby, 2020, ArXiv.

Amortised Variational Inference for Hierarchical Mixture Models

José Miguel Hernández-Lobato, Finale Doshi-Velez, Jiayu Yao, 2020 .

PAC Imitation and Model-based Batch Learning of Contextual MDPs

Finale Doshi-Velez, Yash Nair, 2020 .

Record Time-Series Analysis Comorbidity Clusters in Autism Spectrum Disorders: An Electronic Health

Finale Doshi-Velez, I. Kohane, Y. Ge, 2014 .

Supplement : Semi-Supervised Prediction-Constrained Topic Models

Erik B. Sudderth, Michael C. Hughes, Finale Doshi-Velez, 2018 .

Mitigating Model Non-Identifiability in BNN with Latent Variables

Finale Doshi-Velez, Weiwei Pan, Yaniv Yacoby, 2019 .

Summarizing agent strategies

David Sarne, Ofra Amir, Finale Doshi-Velez, 2019, Autonomous Agents and Multi-Agent Systems.

Big Data in the Assessment of Pediatric Medication Safety

Finale Doshi-Velez, R. Califf, I. Kohane, 2020, Pediatrics.

Regularizing tensor decomposition methods by optimizing pseudo-data

Finale Doshi-Velez, Omer Gottesman, 2018 .

Evaluating Machine Learning Articles.

Finale Doshi-Velez, R. Perlis, F. Doshi-Velez, 2019, JAMA.

Prevalence of Inflammatory Bowel Disease Among Patients with Autism Spectrum Disorders

Kathe P. Fox, Finale Doshi-Velez, I. Kohane, 2015 .

Generating interpretable predictions about antidepressant treatment stability using supervised topic models

Michael C. Hughes, Finale Doshi-Velez, A. Ross, 2020 .

Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models

Michael C. Hughes, Finale Doshi-Velez, A. Ross, 2020, JAMA network open.

Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI

Finale Doshi-Velez, Amit Dhurandhar, Q. Liao, 2022, ArXiv.

Policy Optimization with Sparse Global Contrastive Explanations

Finale Doshi-Velez, S. Parbhoo, Weiwei Pan, 2022, 2207.06269.

Predicting change in diagnosis from major depression to bipolar disorder after antidepressant initiation

Finale Doshi-Velez, T. McCoy, R. Perlis, 2020, Neuropsychopharmacology.

"If it didn't happen, why would I change my decision?": How Judges Respond to Counterfactual Explanations for the Public Safety Assessment

Finale Doshi-Velez, Ben Green, Yaniv Yacoby, 2022, HCOMP.

Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables

Finale Doshi-Velez, Weiwei Pan, Yaniv Yacoby, 2019, J. Mach. Learn. Res..

Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning

Finale Doshi-Velez, Kai Wang, A. Perrault, 2021, ArXiv.

Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care

Kelly W. Zhang, Anna L. Trella, Finale Doshi-Velez, 2022, ArXiv.

Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI

Finale Doshi-Velez, Amit Dhurandhar, Q. Liao, 2022, HCOMP.

Towards Robust Off-Policy Evaluation via Human Inputs

Finale Doshi-Velez, Himabindu Lakkaraju, Shalmali Joshi, 2022, AIES.

Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report

Morgan E. Currie, Michael C. Horowitz, Gillian K. Hadfield, 2022, ArXiv.

Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines

Kelly W. Zhang, Anna L. Trella, Finale Doshi-Velez, 2022, Algorithms.

An interpretable RL framework for pre-deployment modeling in ICU hypotension management

Finale Doshi-Velez, L. Celi, Jianzhun Du, 2022, npj Digital Medicine.

Summarizing agent strategies

Finale Doshi-Velez, Ofra Amir, D. Sarne, 2019, Autonomous Agents and Multi-Agent Systems.

Incorporating Interpretable Output Constraints in Bayesian Neural Networks

Moritz A. Graule, Finale Doshi-Velez, Himabindu Lakkaraju, 2020, NeurIPS.

Accountability of AI Under the Law: The Role of Explanation pdf

David Weinberger, Finale Doshi-Velez, Mason Kortz, 2017, ArXiv.

Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input

Lane Schwartz, Finale Doshi-Velez, William Schuler, 2016, COLING.

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning

Finale Doshi-Velez, Steffen Udluft, José Miguel Hernández-Lobato, 2017, ICML.

Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning

David Pfau, Nicholas Roy, Frank Wood, 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning

David Pfau, Frank D. Wood, Nicholas Roy, 2015, IEEE Trans. Pattern Anal. Mach. Intell..

Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition

Timothy A. Miller, Finale Doshi-Velez, William Schuler, 2021, CL.

Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement

Finale Doshi-Velez, A. Ross, F. Doshi-Velez, 2021, ICML.

Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients

Andrew Slavin Ross, Finale Doshi-Velez, Finale Doshi-Velez, 2017, AAAI.

Evaluating Reinforcement Learning Algorithms in Observational Health Settings pdf

Srivatsan Srinivasan, Finale Doshi-Velez, Li-Wei H. Lehman, 2018, ArXiv.

Combining Kernel and Model Based Learning for HIV Therapy Selection

Finale Doshi-Velez, Volker Roth, Jasmina Bogojeska, 2017, CRI.

Prediction-Constrained Hidden Markov Models for Semi-Supervised Classiﬁcation

Erik B. Sudderth, Finale Doshi-Velez, Gabriel Hope, 2021 .

Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database

Peter Szolovits, Mike Wu, Marzyeh Ghassemi, 2017, J. Am. Medical Informatics Assoc..

Predicting intervention onset in the ICU with switching state space models

Peter Szolovits, Mike Wu, Finale Doshi-Velez, 2017, CRI.

Regional Tree Regularization for Interpretability in Deep Neural Networks

Mike Wu, Finale Doshi-Velez, Volker Roth, 2020, AAAI.

Regional Tree Regularization for Interpretability in Black Box Models pdf

Mike Wu, Finale Doshi-Velez, Volker Roth, 2019, ArXiv.

Learning Predictive and Interpretable Timeseries Summaries from ICU Data pdf

Finale Doshi-Velez, Sonali Parbhoo, Andrew Slavin Ross, 2021, AMIA.

Unfolding physiological state: mortality modelling in intensive care units

Anna Rumshisky, Peter Szolovits, Finale Doshi-Velez, 2014, KDD.

Human-in-the-Loop Interpretability Prior

Samuel J. Gershman, Finale Doshi-Velez, Been Kim, 2018, NeurIPS.

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

Mike Wu, Finale Doshi-Velez, Volker Roth, 2017, AAAI.

Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible pdf

Javier Zazo, Finale Doshi-Velez, Melanie F. Pradier, 2021, ArXiv.

Efficiently identifying individuals at high risk for treatment resistance in major depressive disorder using electronic health records.

Finale Doshi-Velez, R. Perlis, Isaac Lage, 2022, Journal of affective disorders.

Predicting treatment dropout after antidepressant initiation

Finale Doshi-Velez, R. Perlis, M. Pradier, 2020, Translational Psychiatry.

How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection

Maia L. Jacobs, Krzysztof Z Gajos, Finale Doshi-Velez, 2021, Translational Psychiatry.

PoRB-Nets: Poisson Process Radial Basis Function Networks

Finale Doshi-Velez, Beau Coker, Melanie Fernandes Pradier, 2020, UAI.

Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks pdf

Finale Doshi-Velez, Melanie F. Pradier, Beau Coker, 2019, ArXiv.

Prior matters: simple and general methods for evaluating and improving topic quality in topic modeling

Luke W. Miratrix, Finale Doshi-Velez, Angela Fan, 2017, 1701.03227.

Graph-Sparse LDA: A Topic Model with Structured Sparsity

Ryan P. Adams, Byron C. Wallace, Finale Doshi-Velez, 2014, AAAI.

The Indian Buffet Process: Scalable Inference and Extensions

Finale Doshi-Velez, Finale Doshi-Velez, 2009 .

Output-Constrained Bayesian Neural Networks pdf

Srivatsan Srinivasan, Finale Doshi-Velez, Wanqian Yang, 2019, ArXiv.

Model Selection in Bayesian Neural Networks via Horseshoe Priors

Soumya Ghosh, Finale Doshi-Velez, Jiayu Yao, 2017, J. Mach. Learn. Res..

How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation pdf

Emily Chen, Finale Doshi-Velez, Been Kim, 2018, ArXiv.

Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations

Finale Doshi-Velez, George Konidaris, G. Konidaris, 2013, IJCAI.

POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning

Finale Doshi-Velez, Joseph D. Futoma, M. Hughes, 2020, International Conference on Artificial Intelligence and Statistics.

Improving counterfactual reasoning with kernelised dynamic mixing models

Andrew Slavin Ross, Volker Roth, Finale Doshi-Velez, 2018, PloS one.

Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes

Finale Doshi-Velez, George Konidaris, Taylor W. Killian, 2017, AAAI.

Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes pdf

George Dimitri Konidaris, Finale Doshi-Velez, Taylor W. Killian, 2016, ArXiv.

Hidden Parameter Markov Decision Processes: An Emerging Paradigm for Modeling Families of Related Tasks

Finale Doshi-Velez, George Konidaris, G. Konidaris, 2014, AAAI Fall Symposia.

A Comparison of Human and Agent Reinforcement Learning in Partially Observable Domains

Zoubin Ghahramani, Finale Doshi-Velez, Zoubin Ghahramani, 2011, CogSci.

Quality of Uncertainty Quantification for Bayesian Neural Network Inference pdf

Soumya Ghosh, Finale Doshi-Velez, Weiwei Pan, 2019, ArXiv.

Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors

Finale Doshi-Velez, Soumya Ghosh, Jiayu Yao, 2018, ICML.

Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning

Andrew Slavin Ross, Finale Doshi-Velez, Li-Wei H. Lehman, 2018, AMIA.

Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare

Finale Doshi-Velez, J. Wiens, M. Sjoding, 2023, NeurIPS.

Representation Balancing MDPs for Off-Policy Policy Evaluation

Yao Liu, Finale Doshi-Velez, Emma Brunskill, 2018, NeurIPS.

Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens

Krzysztof Z. Gajos, Finale Doshi-Velez, Maia Jacobs, 2021, CHI.

Unsupervised Grammar Induction with Depth-bounded PCFG

Lane Schwartz, Finale Doshi-Velez, William Schuler, 2018, TACL.

Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies pdf

Finale Doshi-Velez, Muhammad A. Masood, Finale Doshi-Velez, 2019, IJCAI.

Exploring Computational User Models for Agent Policy Summarization pdf

Finale Doshi-Velez, Ofra Amir, Isaac Lage, 2019, IJCAI.

Agent Strategy Summarization

David Sarne, Finale Doshi-Velez, Ofra Amir, 2018, AAMAS.

A Bayesian nonparametric approach to modeling motion patterns

Albert S. Huang, Nicholas Roy, Finale Doshi-Velez, 2011, Auton. Robots.

A Bayesian Nonparametric Approach to Modeling Mobility Patterns

Nicholas Roy, Finale Doshi-Velez, Joshua Mason Joseph, 2010, AAAI.

Considerations for Evaluation and Generalization in Interpretable Machine Learning

Been Kim, Finale Doshi-Velez, Finale Doshi-Velez, 2018 .

Guidelines for reinforcement learning in healthcare

Fredrik D. Johansson, Aldo A. Faisal, Finale Doshi-Velez, 2019, Nature Medicine.

Human Evaluation of Models Built for Interpretability

Samuel J. Gershman, Emily Chen, Been Kim, 2019, HCOMP.

Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems pdf

Finale Doshi-Velez, Steffen Udluft, José Miguel Hernández-Lobato, 2017, ArXiv.

Ensembles of Locally Independent Prediction Models

Finale Doshi-Velez, L. Celi, A. Ross, 2019, AAAI.

Learning Qualitatively Diverse and Interpretable Rules for Classification pdf

Andrew Slavin Ross, Finale Doshi-Velez, Weiwei Pan, 2018, ArXiv.

Evaluating the Interpretability of Generative Models by Interactive Reconstruction

Elena L. Glassman, Finale Doshi-Velez, Nina Chen, 2021, CHI.

Learning Deep Bayesian Latent Variable Regression Models that Generalize: When Non-identifiability is a Problem pdf

Finale Doshi-Velez, Weiwei Pan, Yaniv Yacoby, 2019, ArXiv.

Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction pdf

Lane Schwartz, Finale Doshi-Velez, William Schuler, 2018, EMNLP.

Generalization in Clinical Prediction Models: The Blessing and Curse of Measurement Indicator Variables

Finale Doshi-Velez, R. Kamaleswaran, Joseph D. Futoma, 2021, Critical care explorations.

Promises and Pitfalls of Black-Box Concept Learning Models pdf

Finale Doshi-Velez, Weiwei Pan, Isaac Lage, 2021, ArXiv.

Prediction Focused Topic Models via Feature Selection

Finale Doshi-Velez, Jason Ren, R. Kunes, 2019, AISTATS.

Addressing Leakage in Concept Bottleneck Models

Finale Doshi-Velez, S. Parbhoo, Marton Havasi, 2022, NeurIPS.

An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks

Finale Doshi-Velez, Jiayu Yao, Weiwei Pan, 2022, ArXiv.

Travel-time prediction using neural-network-based mixture models

Finale Doshi-Velez, D. Nikovski, Jing Zhang, 2023, Procedia Computer Science.

A Bayesian Framework for Learning Rule Sets for Interpretable Classification

Cynthia Rudin, Finale Doshi-Velez, Tong Wang, 2017, J. Mach. Learn. Res..

Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems pdf

Yimin Liu, Tong Wang, Cynthia Rudin, 2015, ArXiv.

A Bayesian nonparametric approach to modeling battery health

Nicholas Roy, Finale Doshi-Velez, Joshua Mason Joseph, 2012, 2012 IEEE International Conference on Robotics and Automation.

Bayesian Rule Sets for Interpretable Classification

Cynthia Rudin, Finale Doshi-Velez, Tong Wang, 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

Learning Interpretable Concept-Based Models with Human Feedback pdf

Finale Doshi-Velez, Isaac Lage, Finale Doshi-Velez, 2020, ArXiv.

Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs pdf

Finale Doshi-Velez, Joseph Futoma, Jianzhun Du, 2020, NeurIPS.

Learning Under Adversarial and Interventional Shifts pdf

Finale Doshi-Velez, Himabindu Lakkaraju, Shalmali Joshi, 2021, ArXiv.

Power-Constrained Bandits pdf

Finale Doshi-Velez, Weiwei Pan, Susan Murphy, 2020, MLHC.

A Roadmap for a Rigorous Science of Interpretability pdf

Finale Doshi-Velez, Been Kim, Finale Doshi-Velez, 2017, ArXiv.

Learning Optimal Summaries of Clinical Time-series with Concept Bottleneck Models

Finale Doshi-Velez, S. Parbhoo, Marton Havasi, 2022, MLHC.

Unsupervised Learning of PCFGs with Normalizing Flow

Lane Schwartz, Finale Doshi-Velez, William Schuler, 2019, ACL.

Truly Batch Apprenticeship Learning with Deep Successor Features pdf

Srivatsan Srinivasan, Finale Doshi-Velez, Donghun Lee, 2019, IJCAI.

Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities

Harold Soh, Finale Doshi-Velez, Akshay Narayan, 2023, ArXiv.

Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions pdf

Yao Liu, Emma Brunskill, Joseph Futoma, 2020, ICML.

Reinforcement Learning with Limited Reinforcement : Using Bayes Risk for Active Learning in POMDPs Finale

Joelle Pineau, Nicholas Roy, Nicholas A. Roy, 2012 .

Do no harm: a roadmap for responsible machine learning for health care

David C. Kale, Dave C. Kale, S. Saria, 2019, Nature Medicine.