Finale Doshi-Velez
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American computer scientist
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Computer Science
Why Is Finale Doshi-Velez Influential?
(Suggest an Edit or Addition)According to Wikipedia, Finale Doshi-Velez is a computer scientist and the John L. Loeb Professor of Engineering and Applied Sciences at Harvard University. She works on machine learning, computational statistics and healthcare.
Finale Doshi-Velez's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Towards A Rigorous Science of Interpretable Machine Learning (2017) (2448)
- Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients (2017) (506)
- Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations (2017) (413)
- Do no harm: a roadmap for responsible machine learning for health care (2019) (373)
- Comorbidity Clusters in Autism Spectrum Disorders: An Electronic Health Record Time-Series Analysis (2014) (337)
- Accountability of AI Under the Law: The Role of Explanation (2017) (320)
- How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation (2018) (269)
- Guidelines for reinforcement learning in healthcare (2019) (245)
- Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning (2017) (230)
- Beyond Sparsity: Tree Regularization of Deep Models for Interpretability (2017) (210)
- Unfolding physiological state: mortality modelling in intensive care units (2014) (205)
- A Bayesian Framework for Learning Rule Sets for Interpretable Classification (2017) (160)
- Variational Inference for the Indian Buffet Process (2009) (154)
- The myth of generalisability in clinical research and machine learning in health care (2020) (145)
- A Bayesian nonparametric approach to modeling motion patterns (2011) (142)
- Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks (2016) (141)
- A Roadmap for a Rigorous Science of Interpretability (2017) (120)
- The Infinite Partially Observable Markov Decision Process (2009) (111)
- Explainable Reinforcement Learning via Reward Decomposition (2019) (108)
- Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs (2008) (106)
- Considerations for Evaluation and Generalization in Interpretable Machine Learning (2018) (103)
- Human-in-the-Loop Interpretability Prior (2018) (99)
- Evaluating Reinforcement Learning Algorithms in Observational Health Settings (2018) (98)
- Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction (2015) (96)
- Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations (2013) (91)
- Model Selection in Bayesian Neural Networks via Horseshoe Priors (2017) (89)
- Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes (2017) (84)
- Quality of Uncertainty Quantification for Bayesian Neural Network Inference (2019) (78)
- Human Evaluation of Models Built for Interpretability (2019) (77)
- How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection (2021) (73)
- Spoken language interaction with model uncertainty: an adaptive human–robot interaction system (2008) (73)
- Efficient model learning for dialog management (2007) (72)
- Representation Balancing MDPs for Off-Policy Policy Evaluation (2018) (68)
- Accelerated sampling for the Indian Buffet Process (2009) (68)
- Online Discovery of Feature Dependencies (2011) (65)
- Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors (2018) (62)
- Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens (2021) (59)
- Combining Kernel and Model Based Learning for HIV Therapy Selection (2017) (58)
- Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models (2016) (57)
- Predicting intervention onset in the ICU with switching state space models (2017) (55)
- Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning (2018) (54)
- Bayesian Rule Sets for Interpretable Classification (2016) (54)
- Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning (2015) (51)
- Graph-Sparse LDA: A Topic Model with Structured Sparsity (2014) (48)
- Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database (2017) (47)
- Exploring Computational User Models for Agent Policy Summarization (2019) (46)
- Electronic Health Record Based Algorithm to Identify Patients with Autism Spectrum Disorder (2016) (45)
- Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process (2009) (43)
- Nonparametric Bayesian Policy Priors for Reinforcement Learning (2010) (42)
- The permutable POMDP: fast solutions to POMDPs for preference elicitation (2008) (42)
- Prevalence of Inflammatory Bowel Disease Among Patients with Autism Spectrum Disorders (2015) (40)
- Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems (2015) (39)
- A Bayesian Nonparametric Approach to Modeling Mobility Patterns (2010) (38)
- Summarizing agent strategies (2019) (36)
- POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning (2020) (34)
- Correlated Non-Parametric Latent Feature Models (2009) (34)
- Infinite Dynamic Bayesian Networks (2011) (33)
- Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies (2019) (32)
- Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report (2022) (31)
- Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions (2020) (31)
- Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables (2017) (31)
- Semi-Supervised Prediction-Constrained Topic Models (2018) (28)
- Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs (2020) (26)
- PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models (2018) (25)
- Unsupervised Grammar Induction with Depth-bounded PCFG (2018) (25)
- Truly Batch Apprenticeship Learning with Deep Successor Features (2019) (25)
- Promises and Pitfalls of Black-Box Concept Learning Models (2021) (25)
- Evaluating Machine Learning Articles. (2019) (24)
- Unsupervised Learning of PCFGs with Normalizing Flow (2019) (24)
- Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters (2018) (24)
- Direct Policy Transfer via Hidden Parameter Markov Decision Processes (2018) (22)
- Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems (2017) (22)
- Evaluating the Interpretability of Generative Models by Interactive Reconstruction (2021) (21)
- Combining Parametric and Nonparametric Models for Off-Policy Evaluation (2019) (21)
- Regional Tree Regularization for Interpretability in Deep Neural Networks (2020) (20)
- Agent Strategy Summarization (2018) (18)
- Ensembles of Locally Independent Prediction Models (2019) (17)
- Predicting treatment dropout after antidepressant initiation (2020) (17)
- Improving safety and operational efficiency in residential care settings with WiFi-based localization. (2012) (17)
- Optimizing for Interpretability in Deep Neural Networks with Tree Regularization (2019) (17)
- Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI (2022) (15)
- Collision detection in legged locomotion using supervised learning (2007) (13)
- Latent Projection BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights (2018) (13)
- Do no harm: a roadmap for responsible machine learning for health care (2019) (13)
- Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning (2015) (12)
- Power-Constrained Bandits (2020) (12)
- Machine learning approaches to environmental disturbance rejection in multi-axis optoelectronic force sensors (2016) (11)
- Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction (2018) (11)
- Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks (2020) (11)
- Output-Constrained Bayesian Neural Networks (2019) (11)
- Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models (2020) (11)
- Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients (2020) (11)
- Improving counterfactual reasoning with kernelised dynamic mixing models (2018) (10)
- Learning Qualitatively Diverse and Interpretable Rules for Classification (2018) (10)
- Interpretable Batch IRL to Extract Clinician Goals in ICU Hypotension Management. (2020) (10)
- Roll-back Hamiltonian Monte Carlo (2017) (10)
- Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models (2017) (10)
- A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization (2018) (10)
- Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input (2016) (9)
- Toward Robust Policy Summarization (2019) (9)
- Prediction-Constrained Topic Models for Antidepressant Recommendation (2017) (9)
- Cost-Sensitive Batch Mode Active Learning: Designing Astronomical Observation by Optimizing Telescope Time and Telescope Choice (2016) (9)
- Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data (2021) (9)
- Author Correction: Do no harm: a roadmap for responsible machine learning for health care (2019) (9)
- Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning (2021) (9)
- A Characterization of the Non-Uniqueness of Nonnegative Matrix Factorizations (2016) (9)
- Wide Mean-Field Bayesian Neural Networks Ignore the Data (2022) (9)
- The Indian Buffet Process: Scalable Inference and Extensions (2009) (9)
- Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning (2020) (8)
- Assessing topic model relevance: Evaluation and informative priors (2019) (8)
- Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement (2021) (8)
- Prior matters: simple and general methods for evaluating and improving topic quality in topic modeling (2017) (8)
- Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible (2021) (8)
- A Comparison of Human and Agent Reinforcement Learning in Partially Observable Domains (2011) (8)
- Learning Interpretable Concept-Based Models with Human Feedback (2020) (8)
- Hidden Parameter Markov Decision Processes: An Emerging Paradigm for Modeling Families of Related Tasks (2014) (8)
- Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes (2016) (8)
- Incorporating Interpretable Output Constraints in Bayesian Neural Networks (2020) (7)
- Big Data in the Assessment of Pediatric Medication Safety (2020) (7)
- Supervised topic models for clinical interpretability (2016) (7)
- Generalization in Clinical Prediction Models: The Blessing and Curse of Measurement Indicator Variables (2021) (7)
- Machine Learning Techniques for Accountability (2021) (7)
- Prediction Focused Topic Models via Feature Selection (2019) (6)
- Regional Tree Regularization for Interpretability in Black Box Models (2019) (6)
- Predicting change in diagnosis from major depression to bipolar disorder after antidepressant initiation (2020) (6)
- The Role of Explanation in Algorithmic Trust ∗ Finale Doshi-Velez Ryan Budish Mason Kortz (2017) (6)
- Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights (2019) (6)
- A Bayesian nonparametric approach to modeling battery health (2012) (6)
- BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty (2020) (5)
- Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning (2021) (5)
- Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders (2016) (5)
- Restricted Indian buffet processes (2015) (5)
- Defining admissible rewards for high-confidence policy evaluation in batch reinforcement learning (2019) (4)
- Transfer Learning by Discovering Latent Task Parametrizations (2012) (4)
- Bayesian nonparametric approaches for reinforcement learning in partially observable domains (2012) (4)
- Efficiently identifying individuals at high risk for treatment resistance in major depressive disorder using electronic health records. (2022) (4)
- Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines (2022) (4)
- An Empirical Comparison of Sampling Quality Metrics: A Case Study for Bayesian Nonnegative Matrix Factorization (2016) (4)
- Spectral M-estimation with Applications to Hidden Markov Models (2016) (4)
- Challenges in Computing and Optimizing Upper Bounds of Marginal Likelihood based on Chi-Square Divergences (2019) (4)
- Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks (2019) (3)
- "If it didn't happen, why would I change my decision?": How Judges Respond to Counterfactual Explanations for the Public Safety Assessment (2022) (3)
- Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders (2020) (3)
- Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition (2021) (3)
- Do clinicians follow heuristics in prescribing antidepressants? (2022) (3)
- Promoting Domain-Specific Terms in Topic Models with Informative Priors (2017) (3)
- Prediction Focused Topic Models via Vocab Selection (2019) (3)
- Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks (2020) (2)
- Learning Under Adversarial and Interventional Shifts (2021) (2)
- The application of machine learning methods to evaluate predictors of live birth in programmed thaw cycles (2019) (2)
- Artificial Intelligence & Cooperation (2020) (2)
- On Learning Prediction-Focused Mixtures (2021) (2)
- Learning Deep Bayesian Latent Variable Regression Models that Generalize: When Non-identifiability is a Problem (2019) (2)
- Learned Uncertainty-Aware (LUNA) Bases for Bayesian Regression using Multi-Headed Auxiliary Networks (2020) (2)
- Prediction-Constrained Hidden Markov Models for Semi-Supervised Classification (2021) (2)
- Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare (2023) (2)
- What Makes a Good Explanation?: A Harmonized View of Properties of Explanations (2022) (2)
- Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty (2021) (2)
- PAC Bounds for Imitation and Model-based Batch Learning of Contextual Markov Decision Processes (2020) (2)
- Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Sepsis Treatment (2020) (1)
- PoRB-Nets: Poisson Process Radial Basis Function Networks (2020) (1)
- Towards Robust Off-Policy Evaluation via Human Inputs (2022) (1)
- Reports of the AAAI 2011 Spring Symposia (2011) (1)
- Online structural kernel selection for mobile health (2021) (1)
- Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making (2022) (1)
- Record Time-Series Analysis Comorbidity Clusters in Autism Spectrum Disorders: An Electronic Health (2014) (1)
- Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment (2021) (1)
- Supplement : Semi-Supervised Prediction-Constrained Topic Models (2018) (1)
- Mitigating Model Non-Identifiability in BNN with Latent Variables (2019) (1)
- Shaping Control Variates for Off-Policy Evaluation (2020) (1)
- Policy Optimization with Sparse Global Contrastive Explanations (2022) (1)
- Addressing Leakage in Concept Bottleneck Models (2022) (1)
- Guidelines for reinforcement learning in healthcare (2019) (1)
- Weighted Tensor Decomposition for Learning Latent Variables with Partial Data (2017) (1)
- Prediction Focused Topic Models for Electronic Health Records (2019) (1)
- Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning (2021) (1)
- State Relevance for Off-Policy Evaluation (2021) (1)
- Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care (2022) (1)
- Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation (2021) (1)
- A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes (2022) (1)
- Learning Predictive and Interpretable Timeseries Summaries from ICU Data (2021) (1)
- Robust Decision-Focused Learning for Reward Transfer (2023) (0)
- Active Screening on Recurrent Diseases Contact Networks with Uncertainty: A Reinforcement Learning Approach (2020) (0)
- Prediction-focused Mixture Models (2021) (0)
- Summarizing agent strategies (2019) (0)
- Diversity-Inducing Policy Gradient : Using MMD to find a set of policies that are diverse in terms of state-visitation (2018) (0)
- Projected BNNs : Avoiding weight-space pathologies by projecting neural network weights (2018) (0)
- An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks (2022) (0)
- Amortised Variational Inference for Hierarchical Mixture Models (2020) (0)
- Regularizing tensor decomposition methods by optimizing pseudo-data (2018) (0)
- Learning Optimal Summaries of Clinical Time-series with Concept Bottleneck Models (2022) (0)
- (When) Are Contrastive Explanations of Reinforcement Learning Helpful? (2022) (0)
- Supplemental Material for Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement (2021) (0)
- A general method for regularizing tensor decomposition methods via pseudo-data (2019) (0)
- Modeling Mobile Health Users as Reinforcement Learning Agents (2022) (0)
- 1185-P: Predictive Model for Hyperglycemic Events after High Dose Corticosteroid Administration (2019) (0)
- Does the explanation satisfy your needs?: A unified view of properties of explanations (2022) (0)
- Learning-to-defer for sequential medical decision-making under uncertainty (2021) (0)
- Nonparametric Bayesian Approaches for Reinforcement Learning in Partially Observable Domains (2010) (0)
- Travel-time prediction using neural-network-based mixture models (2023) (0)
- Rapid Posterior Exploration in Bayesian Non-negative Matrix Factorization (2016) (0)
- Power Constrained Bandit (2021) (0)
- Restricted Indian buffet processes (2016) (0)
- Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry (2022) (0)
- A Joint Learning Approach for Semi-supervised Neural Topic Modeling (2022) (0)
- Computational physiology : papers from the AAAI Spring Symposium (2011) (0)
- An interpretable RL framework for pre-deployment modeling in ICU hypotension management (2022) (0)
- PAC Imitation and Model-based Batch Learning of Contextual MDPs (2020) (0)
- Organizing committee (2000) (0)
- P ERFORMANCE B OUNDS FOR M ODEL AND P OLICY T RANSFER IN H IDDEN - PARAMETER MDP S (2023) (0)
- Generating interpretable predictions about antidepressant treatment stability using supervised topic models (2020) (0)
- Bayesian nonparametric methods for reinforcement learning in partially observable domains (2012) (0)
- Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables (2019) (0)
- Stitched Trajectories for Off-Policy Learning (2018) (0)
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