Wiens, Jenna

40 publications

TMLR 2026 Fast Graph Generation via Autoregressive Noisy Filtration Modeling Markus Krimmel, Jenna Wiens, Karsten Borgwardt, Dexiong Chen

CHIL 2025 Conditional Front-Door Adjustment for Heterogeneous Treatment Assignment Effect Estimation Under Non-Compliance Winston Chen, Trenton Chang, Jenna Wiens

NeurIPS 2025 Disentangling Misreporting from Genuine Adaptation in Strategic Settings: A Causal Approach Dylan Zapzalka, Trenton Chang, Lindsay Warrenburg, Sae-Hwan Park, Daniel K Shenfeld, Ravi B Parikh, Jenna Wiens, Maggie Makar

AISTATS 2025 Learning Laplacian Positional Encodings for Heterophilous Graphs Michael Ito, Jiong Zhu, Dexiong Chen, Danai Koutra, Jenna Wiens

NeurIPS 2025 Random Search Neural Networks for Efficient and Expressive Graph Learning Michael Ito, Danai Koutra, Jenna Wiens

AISTATS 2025 Understanding GNNs and Homophily in Dynamic Node Classification Michael Ito, Danai Koutra, Jenna Wiens

ECCV 2024 DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks Sarah Jabbour, Gregory Kondas, Ella Kazerooni, Michael Sjoding, David Fouhey, Jenna Wiens

ICML 2024 From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions Trenton Chang, Jenna Wiens

AISTATS 2024 Learning to Rank for Optimal Treatment Allocation Under Resource Constraints Fahad Kamran, Maggie Makar, Jenna Wiens

NeurIPSW 2024 Measuring Steerability in Large Language Models Trenton Chang, Jenna Wiens, Tobias Schnabel, Adith Swaminathan

CHIL 2024 Multiple Instance Learning with Absolute Position Information Meera Krishnamoorthy, Jenna Wiens

NeurIPS 2024 Who’s Gaming the System? a Causally-Motivated Approach for Detecting Strategic Adaptation Trenton Chang, Lindsay Warrenburg, Sae-Hwan Park, Ravi B. Parikh, Maggie Makar, Jenna Wiens

NeurIPS 2023 Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation Shengpu Tang, Jenna Wiens

CHIL 2023 Denoising Autoencoders for Learning from Noisy Patient-Reported Data Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens

AAAI 2023 Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens

ICMLW 2023 Leveraging Factored Action Spaces for Off-Policy Evaluation Aaman Peter Rebello, Shengpu Tang, Jenna Wiens, Sonali Parbhoo

CHIL 2023 Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise Donna Tjandra, Jenna Wiens

MLHC 2023 Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance Erkin Ötles, Brian T. Denton, Jenna Wiens

MLHC 2022 Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning Trenton Chang, Michael W. Sjoding, Jenna Wiens

NeurIPS 2022 Learning Concept Credible Models for Mitigating Shortcuts Jiaxuan Wang, Sarah Jabbour, Maggie Makar, Michael Sjoding, Jenna Wiens

NeurIPS 2022 Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare Shengpu Tang, Maggie Makar, Michael Sjoding, Finale Doshi-Velez, Jenna Wiens

ICMLW 2022 Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare Shengpu Tang, Maggie Makar, Michael Sjoding, Finale Doshi-Velez, Jenna Wiens

AISTATS 2021 Shapley Flow: A Graph-Based Approach to Interpreting Model Predictions Jiaxuan Wang, Jenna Wiens, Scott Lundberg

AAAI 2021 A Hierarchical Approach to Multi-Event Survival Analysis Donna Tjandra, Yifei He, Jenna Wiens

AAAI 2021 Estimating Calibrated Individualized Survival Curves with Deep Learning Fahad Kamran, Jenna Wiens

MLHC 2021 Mind the Performance Gap: Examining Dataset Shift During Prospective Validation Erkin Otles, Jeeheh Oh, Benjamin Li, Michelle Bochinski, Hyeon Joo, Justin Ortwine, Erica Shenoy, Laraine Washer, Vincent B. Young, Krishna Rao, Jenna Wiens

MLHC 2021 Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings Shengpu Tang, Jenna Wiens

ICML 2020 Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies Shengpu Tang, Aditya Modi, Michael Sjoding, Jenna Wiens

MLHC 2020 Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts Sarah Jabbour, David Fouhey, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens

MLHC 2020 Deep Reinforcement Learning for Closed-Loop Blood Glucose Control Ian Fox, Joyce Lee, Rodica Pop-Busui, Jenna Wiens

IJCAI 2019 Advocacy Learning: Learning Through Competition and Class-Conditional Representations Ian Fox, Jenna Wiens

ICMLW 2019 Reinforcement Learning for Blood Glucose Control: Challenges and Opportunities Ian Fox, Jenna Wiens

MLHC 2019 Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series Jeeheh Oh, Jiaxuan Wang, Shengpu Tang, Michael W. Sjoding, Jenna Wiens

MLHC 2018 A Domain Guided CNN Architecture for Predicting Age from Structural Brain Images Pascal Sturmfels, Saige Rutherford, Mike Angstadt, Mark Peterson, Chandra Sripada, Jenna Wiens

AAAI 2018 Learning the Probability of Activation in the Presence of Latent Spreaders Maggie Makar, John V. Guttag, Jenna Wiens

MLHC 2018 Learning to Exploit Invariances in Clinical Time-Series Data Using Sequence Transformer Networks Jeeheh Oh, Jiaxuan Wang, Jenna Wiens

MLJ 2016 Editorial: Special Issue on Machine Learning for Health and Medicine Jenna Wiens, Byron C. Wallace

JMLR 2016 Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach Jenna Wiens, John Guttag, Eric Horvitz

NeurIPS 2012 Patient Risk Stratification for Hospital-Associated C. Diff as a Time-Series Classification Task Jenna Wiens, Eric Horvitz, John V. Guttag

NeurIPS 2010 Active Learning Applied to Patient-Adaptive Heartbeat Classification Jenna Wiens, John V. Guttag