| 2026 | AAAI | A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs. | Trenton Chang, Tobias Schnabel, Adith Swaminathan, Jenna Wiens |
| 2026 | AAAI | Measuring Model Performance in the Presence of an Intervention. | Winston Chen, Michael W. Sjoding, Jenna Wiens |
| 2025 | AISTATS | Understanding GNNs and Homophily in Dynamic Node Classification. | Michael Ito, Danai Koutra, Jenna Wiens |
| 2025 | AISTATS | Learning Laplacian Positional Encodings for Heterophilous Graphs. | Michael Ito, Jiong Zhu, Dexiong Chen, Danai Koutra, Jenna Wiens |
| 2025 | KDD | Cross-Validation for Longitudinal Datasets with Unstable Correlations. | Meera Krishnamoorthy, Michael W. Sjoding, Jenna Wiens |
| 2024 | AISTATS | Learning to Rank for Optimal Treatment Allocation Under Resource Constraints. | Fahad Kamran, Maggie Makar, Jenna Wiens |
| 2024 | ECCV | DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks. | Sarah Jabbour, Gregory Kondas, Ella Kazerooni, Michael W. Sjoding, David Fouhey, Jenna Wiens |
| 2024 | ICDM | Survival Analysis with Multiple Noisy Labels. | Donna Tjandra, Jenna Wiens |
| 2024 | ICML | From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions. | Trenton Chang, Jenna Wiens |
| 2023 | AAAI | Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose. | Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens |
| 2021 | AAAI | Estimating Calibrated Individualized Survival Curves with Deep Learning. | Fahad Kamran, Jenna Wiens |
| 2021 | AAAI | A Hierarchical Approach to Multi-Event Survival Analysis. | Donna Tjandra, Yifei He, Jenna Wiens |
| 2021 | AISTATS | Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. | Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg |
| 2020 | ECAI | Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction. | Harry Rubin-Falcone, Ian Fox, Jenna Wiens |
| 2020 | ICML | Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. | Shengpu Tang, Aditya Modi, Michael W. Sjoding, Jenna Wiens |
| 2019 | IJCAI | Advocacy Learning: Learning through Competition and Class-Conditional Representations. | Ian Fox, Jenna Wiens |
| 2018 | AAAI | Learning the Probability of Activation in the Presence of Latent Spreaders. | Maggie Makar, John V. Guttag, Jenna Wiens |
| 2018 | KDD | Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories. | Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens |
| 2018 | KDD | Learning Credible Models. | Jiaxuan Wang, Jeeheh Oh, Haozhu Wang, Jenna Wiens |
| 2017 | AMIA | Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale. | Eli Sherman, Hitinder S. Gurm, Ulysses J. Balis, Scott R. Owens, Jenna Wiens |
| 2017 | KDD | Contextual Motifs: Increasing the Utility of Motifs using Contextual Data. | Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens |
| 2015 | AMIA | Learning Useful Abstractions from the Web. | Abhishek Bafna, Jenna Wiens |
| 2015 | ICDM | Automated Feature Learning: Mining Unstructured Data for Useful Abstractions. | Abhishek Bafna, Jenna Wiens |
| 2014 | AAAI | Preface. | Finale Doshi-Velez, David C. Kale, Byron C. Wallace, Jenna Wiens |