# Recent Publications

You can find DtAK publications from past years at Finale's Google Scholar profile.

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

*JMLR*, 2022 [pdf]

**"If it didnâ€™t happen, why would I change my decision?": How Judges Respond to Counterfactual Explanations for the Public Safety Assessment**

*HCOMP 2022 and CHI Workshop on Human Centered Explainable AI (HCXAI)*, 2022 [pdf]

**Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making**

*Preprint*, 2022 [pdf]

**Learning Predictive and Interpretable Timeseries Summaries from ICU Data**

*In Proceedings of AMIA Annual Symposium*, 2021 [pdf]

**On formalizing causal off-policy evaluation for sequential decision-making**

*proceedings at the International Conference on Machine Learning: Workshop on Neglected Assumptions in Causal Inference*, 2021

**Pre-emptive Learning to Defer for Sequential Medical Decision-Making Under Uncertainty**

*proceedings at the International Conference on Machine Learning: Workshop on Interpretable Machine Learning for Healthcare*, 2021 [pdf]

**Optimizing for Interpretability in Deep Neural Networks with Tree Regularization**

*Journal of AI Research (JAIR)*, 2021 [pdf]

**Promises and Pitfalls of Black-Box Concept Learning Models**

*proceedings at the Internation Conference on Machine Learning: Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI*, 2021 [pdf]

**Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data**

*proceedings at the International Conference on Machine Learning: Workshop on Uncertainty & Robustness in Deep Learning (ICML)*, 2021 [pdf]

**Prediction-focused Mixture Models**

*proceedings at the International Conference on Machine Learning: Information-Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning (ICML)*, 2021 [pdf]

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

*Critical Care Explorations*, 2021 [pdf]

**Power Constrained Bandit**

*proceedings at the Machine Learning for Healthcare Conference*, 2021 [pdf]

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

*Preprint*, 2021 [pdf]

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

*proceedings at the 2021 CHI Conference on Human Factors in Computing Systems*, 2021 [pdf]

**Evaluating the Interpretability of Generative Models by Interactive Reconstruction**

*proceedings at the 2021 CHI Conference on Human Factors in Computing Systems*, 2021 [pdf]

**Machine Learning Techniques for Accountability**

*AI Magazine*, 2021 [pdf]

**Learning Under Adversarial and Interventional Shifts**

*Preprint*, 2021 [pdf]

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

*Computational Linguistics*, 2021 [pdf]

**Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement**

*Preprint*, 2021 [pdf]

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

*Translational psychiatry*, 2021 [pdf]

**Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise As Much As Possible**

*Proceedings of Clinical Research Informatics AMIA Summit*, 2021 [pdf]

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

*Neuropsychopharmacology*, 2021 [pdf]

**Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment**

*proceedings at the Neural Information Processing Systems Workshop on Machine Learning for Health*, 2020 [pdf]

**Shaping Control Variates for Off-Policy Evaluation**

*proceedings at Neural Information Processing Systems Workshop on Offline Reinforcement Learning*, 2020

**Artificial Intelligence & Cooperation**

*Preprint*, 2020 [pdf]

**Learning Interpretable Concept-Based Models with Human Feedback**

*proceedings at the International Conference on Machine Learning: Workshop on Human Interpretability in Machine Learning*, 2020 [pdf]

**Interpretable off-policy evaluation in reinforcement learning by highlighting influential transitions**

*proceedings at the International Conference on Machine Learning*, 2020 [pdf]

**Incorporating Interpretable Output Constraints in Bayesian Neural Networks**

*proceedings at the Conference on Neural Information Processing Systems*, 2020 [pdf]

**Transfer learning from well-curated to less-resourced populations with hiv**

*proceedings at the Machine Learning for Health Care Conference*, 2020 [pdf]

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

*The Lancet Digital Health*, 2020 [pdf]

**PoRB-Nets: Poisson Process Radial Basis Function Networks**

*proceedings at the Conference on Uncertainty in Artificial Intelligence*, 2020 [pdf]

**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]

**CRUDS: Counterfactual Recourse Using Disentangled Subspaces**

*proceedings at the International Conference on Machine Learning: Workshop on Human Interpretability in Machine Learning*, 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]

**Amortised Variational Inference for Hierarchical Mixture Models**

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

**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]

**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]