# Publications

You can also find many DtAK publications on Finale's Google Scholar profile.

**Failures of Variational Autoencoders and their Effects on Downstream Tasks**

*proceedings at the International Conference on Machine Learning: Workshop on Uncertainty & Robustness in Deep Learning (ICML)*, 2020 [pdf]

**BaCOUn - Bayesian Classifers with Out-of-Distribution Uncertainty**

*proceedings at the International Conference on Machine Learning: Workshop on Uncertainty & Robustness in Deep Learning (ICML)*, 2020 [pdf]

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

*proceedings at the International Conference on Machine Learning: Workshop on Uncertainty & Robustness in Deep Learning (ICML)*, 2020 [pdf]

**Power-Constrained Bandit**

*arXiv:2004.06230*, 2020 [pdf]

**Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders**

*Advances in Approximate Bayesian Inference*, 2019 [pdf]

**Defining Admissible Rewards for High-Confidence Policy Evaluation in Batch Reinforcement Learning**

*ACM Conference on Health, Inference and Learning*, 2020 [pdf]

**Prediction Focused Topic Models via Feature Selection**

*AISTATS*, 2020 [pdf]

**POPCORN: Partially Observed Prediction Constrained Reinforcement Learning**

*AISTATS*, 2020 [pdf]

**Regional Tree Regularization for Interpretability in Deep Neural Networks**

*AAAI*, 2020 [pdf]

**Regional Tree Regularization for Interpretability in Deep Neural Networks**

*AAAI*, 2020 [pdf]

**Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning**

*AMIA CRI*, 2020 [pdf]

**Interpretable Batch IRL to extract clinician goals in ICU Hypotension Management**

*AMIA CRI*, 2020 [pdf]

**Big Data in the Assessment of Pediatric Medication Safety**

*Pediatrics*, 2020 [pdf]

**Evaluating Machine Learning Articles**

*JAMA*, 2020 [pdf]

**Predicting treatment dropout after antidepressant initiation**

*Translational Psychiatry*, 2020 [pdf]

**A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization**

*Journal of Machine Learning Research*, 2019 [pdf]

**Model Selection in Bayesian Neural Networks via Horseshoe Priors**

*Journal of Machine Learning Research*, 2019 [pdf]

**Defining Admissible Rewards for High Confidence Policy Evaluation**

*NeurIPS Workshop on Safety and Robustness in Decision-Making,*, 2019 [pdf]

**Controlled Direct Effect Priors for Bayesian Neural Networks**

*NeurIPS Workshop on Bayesian Deep Learning*, 2019 [pdf]

**Integrating AI Recommendations into The Pharmacologic Management of Major Depressive Disorder**

*CSCW Workshop: Identifying Challenges and Opportunities in Humanâ€“AI Collaboration in Healthcare*, 2019 [pdf]

**Prediction Focused Topic Models Via Vocab Filtering**

*NeurIPS Workshop on Human-Centric ML*, 2019 [pdf]

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

*Advances in Approximate Bayesian Inference*, 2019 [pdf]

**Projected BNNs: Avoiding Weight-space Pathologies by Learning Latent Representations of Neural Network Weights**

*ACML Workshop on Weakly Supervised Learning Workshop*, 2019 [pdf]

**Prediction Focused Topic Models for Electronic Health Records**

*NeurIPS Workshop on Machine Learning for Health*, [pdf]

**Do no harm: A roadmap for responsible machine learning for healthcare**

*Nature Medicine*, [pdf]

**Summarizing Agent Strategies**

*Journal of Autonomous Agents and Multi-Agent Systems (AAMAS)*, [pdf]

**The Application of Machine Learning Methods to Evaluate Predictors for Live Birth in Programmed Thaw Cycles**

*proceedings at the American Society for Reproductive Medicine Scientific Congress & Expo (ASRM)*, [pdf]

**Output-Constrained Bayesian Neural Network**

*proceedings at the International Conference on Machine Learning: Workshop on Understanding and Improving Generalization in Deep Learning(ICML)*, [pdf]

**Output-Constrained Bayesian Neural Networks**

**Mitigating Model Non-Identifiability in BNN with Latent Variables**

**Quality of Uncertainty Quantification for Bayesian Neural Network Inference**

**Poisson Process Bayesian Neural Networks**

*proceedings at the International Conference on Bayesian Nonparametrics (BNP)*, [pdf]

**Toward Robust Policy Summarization**

*proceedings at the International Joint Conference on Artificial Intelligence (IJCAI)*, [pdf]

**Toward Robust Summarization of Agent Policies**

*proceedings at the International Conference on Autonomous Agents and Multiagent Systems (AAMAS)*, [pdf]

**Human Evaluation of Models Built for Interpretability**

*proceedings at the 7th AAAI Conference on Human Computation and Crowdsourcing (HCOMP)*, [pdf]

**Truly Batch Apprenticeship Learning with Deep Successor Features**

*proceedings at the International Joint Conference on Artificial Intelligence (IJCAI)*, [pdf]

**Exploring Computational User Models for Agent Policy Summarization**

*proceedings at the International Joint Conference on Artificial Intelligence: Workshop on Explainable Artificial Intelligence (IJCAI),*, [pdf]

**Explainable Reinforcement Learning via Reward Decomposition**

*proceedings at the International Joint Conference on Artificial Intelligence*, [pdf]

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

*proceedings at the International Joint Conference on Artificial Intelligence (IJCAI)*, [pdf]

**Combining Parametric and Nonparametric Models for off-policy evaluation**

*International Conference on Machine Learning (IMCL)*, [pdf]

**Assessing topic model relevance: Evaluation and informative priors**

*Statistical Analysis and Data Mining*, [pdf]

**Guidelines for reinforcement learning in healthcare**

*Nature Medicine*, [pdf]

**An Evaluation of the Human-Interpretability of Explanation**

*Conference on Neural Information Processing Systems (NeurIPS) Workshop on Correcting and Critiquing Trends in Machine Learning*, 2018 [pdf]

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

*Conference on Neural Information Processing Systems (NeurIPS) Workshop on Bayesian Deep Learning*, [pdf]

**Hierarchical Stick-breaking Feature Paintbox**

*Conference on Neural Information Processing Systems (NeurIPS) Workshop on All of Bayesian Nonparametrics*, [pdf]

**Prediction-Constrained POMDPs**

*Conference on Neural Information Processing Systems (NeurIPS) Workshop on Reinforcement Learning under Partial Observability*, [pdf]

**Improving counterfactual reasoning with kernelised dynamic mixing models**

*PLoS ONE*, [pdf]

**Human-in-the-Loop Interpretability Prior**

*Conference on Neural Information Processing Systems (NeurIPS)*, [pdf]

**Beyond Sparsity: Tree Regularization of Deep Models for Interpretability**

*Association for the Advancement of Artificial Intelligence (AAAI)*, [pdf]

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

*International Conference on Machine Learning (ICML) Exploration in Reinforcement Learning Workshop*, [pdf]

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

*American Medical Informatics Association (AMIA) Annual Symposium*, [pdf]

**Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors**

*Proceedings of the 35th International Conference on Machine Learning (ICML)*, [pdf]

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

*Proceedings of the 35th International Conference on Machine Learning (ICML)*, [pdf]

**Weighted Tensor Decomposition for Learning Latent Variables with Partial Data**

*Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018*, [pdf]

**Semi-Supervised Prediction-Constrained Topic Models**

*Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018*, [pdf]

**Agent Strategy Summarization**

*Autonomous Agents and Multiagent Systems, Blue Sky Ideas Track*, [pdf]

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

*Privacy Law Scholars Conference*, [pdf]

**Unsupervised Grammar Induction with Depth-bounded PCFG**

*Association for Computational Linguistics*, [pdf]

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

*Association for the Advancement of Artificial Intelligence (AAAI)*, [pdf]

**Direct Policy Transfer via Hidden Parameter Markov Decision Processes**

*International Conference on Machine Learning (ICML) Workshop on Lifelong Learning,*, [pdf]

**Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters**

*International Conference on Machine Learning (ICML) Workshop on CausalML*, [pdf]

**Stitched Trajectories for Off-Policy Learning**

*International Conference on Machine Learning (ICML) Workshop on CausalML,*, [pdf]

**Representation Balancing MDPs for Off-Policy Policy Evaluation**

*International Conference on Machine Learning (ICML) Workshop on CausalML*, [pdf]

**Regularizing Tensor Decomposition Methods by Optimizing Pseudo-Data**

*International Conference on Machine Learning (ICML) Exploration in Reinforcement Learning Workshop,*, [pdf]

**Learning Qualitatively Diverse and Interpretable Rules for Classification**

*International Conference on Machine Learning (ICML) Workshop on Human Interpretability in Machine Learning,*, [pdf]

**Depth-bounding is effective: Improvements and Evaluation of Unsupervised PCFG Induction**

*Conference on Empirical Methods in Natural Language Processing (EMNLP)*, [pdf]

**Considerations for Evaluation and Generalization in Interpretable Machine Learning**

*Doshi-Velez F, Kim B*, [pdf]

**PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models**

*IEEE Transactions on Visualization and Computer Graphics*, [pdf]

**Predicting intervention onset in the ICU with switching state space models**

*American Medical Informatics Association (AMIA),*, 2017 [pdf]

**PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models**

*Conference on Visual Analytics Science and Technology (VAST),*, 2017 [pdf]

**The Neural LASSO: Local Linear Sparsity for Interpretable Explanations**

*Neural Information Processing Systems (NIPS) Workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments*, 2017 [pdf]

**Beyond Sparsity: Tree Regularization of Deep Models for Interpretability**

*Neural Information Processing Systems (NIPS) Workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments*, 2017 [pdf]

**Counterfactual Reasoning with Dynamic Switching Models for HIV Therapy Selection**

*Neural Information Processing Systems (NIPS) Workshop on Machine Learning for Healthcare*, 2017 [pdf]

**Prediction-Constrained Topic Models for Antidepressant Recommendation**

*Neural Information Processing Systems (NIPS) Workshop on Machine Learning for Healthcare*, 2017 [pdf]

**Model Selection in Bayesian Neural Networks via Horseshoe Priors**

*Neural Information Processing Systems (NIPS) Workshop on Bayesian Deep Learning*, 2017 [pdf]

**Structured Variational Autoencoders for the Beta-Bernoulli Process**

*Neural Information Processing Systems (NIPS) Workshop on Advances in Approximate Bayesian Inference*, 2017 [pdf]

**Clustering LaTeX Solutions to Machine Learning Assignments for Rapid Assessment**

*Advancing Education with Data Knowledge Discovery and Data Mining (KDD) Workshop*, 2017 [pdf]

**Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables**

*International Conference on Machine Learning (ICML) Workshop*, 2017 [pdf]

**Combining Kernel and Model Based Learning for HIV Therapy Selection**

*AMIA Summits on Translational Science Proceedings*, 2017 [pdf]

**Prior Matters: Simple and General Methods for Evaluating and Improving Topic Quality in Topic Modeling**

*Text as Data*, 2017 [pdf]

**Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes**

*Neural Information Processing Systems (NIPS)*, 2017 [pdf]

**A Bayesian Framework for Learning Rule Sets for Interpretable Classification**

*Journal of Machine Learning*, 2017 [pdf]

**Right for the Right Reasons: Training Differentiable Models by Constraining their Explananations**

*International Joint Conference on Artificial Intelligence (IJCAI)*, 2017 [pdf]

**Restricted Indian Buffet Processes**

*Statistics and Computing*, [pdf]

**Understanding Vasopressor Intervention and Weaning: Risk Prediction in a Public Heterogeneous Clinical Time Series Database**

*Journal of the American Medical Informatics Association*, [pdf]

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

*ICLR*, [pdf]

**Combining Kernel and Model Based Learning for HIV Therapy Selection**

*Neural Information Processing Systems (NIPS) Workshop for Machine Learning and Healthcare*, 2016 [pdf]

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

*Neural Information Processing Systems (NIPS) Workshop for Machine Learning and Healthcare*, 2016 [pdf]

**Supervised topic models for clinical interpretability**

*Neural Information Processing Systems (NIPS) Workshop for Machine Learning and Healthcare*, 2016 [pdf]

**Robust Posterior Exploration in NMF**

*International Conference on Machine Learning (ICML) Workshop on Geometry in Machine Learning*, 2016 [pdf]

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

*Computational Linguistics: Technical Papers (COLING)*, 2016 [pdf]

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

*IJCAI*, 2016 [pdf]

**Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders**

*Journal of Machine Learning Research*, 2016 [pdf]

**Spectral M-estimation with Application to Hidden Markov Models: Supplementary Material**

*AISTATS*, 2016 [pdf]

**A Characterization of the Non-Uniqueness of Nonnegative Matrix Factorizations**

*arXiv:1604.00653*, 2016 [pdf]

**Cost-Sensitive Batch Mode Active Learning: Designing Astronomical Observation by Optimizing Telescope Time and Telescope Choice**

*2016*, 2016 [pdf]

**An Empirical Comparison of Sampling Quality Metrics: A Case Study for Bayesian Nonnegative Matrix Factorization**

*arXiv preprint arXiv:1606.06250*, 2016 [pdf]

**Machine Learning Approaches to Environmental Disturbance Rejection in Multi-Axis Optoelectronic Force Sensors**

*Sensors and Actuators A: Physical*, 2016 [pdf]

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

*PLoS ONE 11(7): e0159621*, 2016 [pdf]

**Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models**

*arXiv:1606.05320*, 2016 [pdf]

**Bayesian Or's of And's for Interpretable Classification with Application to Context Aware Recommender Systems**

*arXiv:1504.07614*, 2015 [pdf]

**Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction**

*Advances in Neural Information Processing Systems*, 2015 [pdf]

**Prevalence of Inflammatory Bowel Disease Among Patients with Autism Spectrum Disorders**

*Inflammatory bowel diseases*, 2015 [pdf]

**Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning**

*IEEE Transactions on Pattern Analysis and Machine Intelligence*, [pdf]

**HackEbola with Data: On the hackathon format for timely data analysis.**

*2015*, [pdf]

**Graph-Sparse LDA: A Topic Model with Structured Sparsity**

*AAAI*, [pdf]

**Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis**

*Pediatrics*, 2014 [pdf]

**Graph-Sparse LDA: A Topic Model with Structured Sparsity**

*arXiv:1410.4510*, 2014 [pdf]

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

*AAAI 2014 Fall Symposium on Knowledge, Skill, and Behavior Transfer in Autonomous Robots*, 2014 [pdf]

**Unfolding Physiological State: Mortality Modelling in Intensive Care Units**

*ACM SIGKDD international conference on Knowledge discovery and data mining*, 2014 [pdf]