Active Learning Applied to Patient-Adaptive Heartbeat Classification. - Jenna Wiens; John V. Guttag (NIPS, 2010)

Patient-specific ventricular beat classification without patient-specific expert knowledge: A transfer learning approach. - Jenna Wiens; John V. Guttag (EMBC, 2011)

Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task. - Jenna Wiens; John V. Guttag; Eric Horvitz (NIPS, 2012)

Preface. - Finale Doshi-Velez; David C. Kale; Byron C. Wallace; Jenna Wiens (AAAI Workshop: Modern Artificial Intelligence for Health Analytics, 2014)

A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions. - Jenna Wiens; John V. Guttag; Eric Horvitz (J. Am. Medical Informatics Assoc., 2014)

Learning to prevent healthcare-associated infections: leveraging data across time and space to improve local predictions. - Jenna Wiens (2014)

Learning Useful Abstractions from the Web. - Abhishek Bafna; Jenna Wiens (AMIA, 2015)

Automated Feature Learning: Mining Unstructured Data for Useful Abstractions. - Abhishek Bafna; Jenna Wiens (ICDM, 2015)

Reports of the AAAI 2014 Conference Workshops. - Stefano V. Albrecht; André da Motta Salles Barreto; Darius Braziunas; David L. Buckeridge; Heriberto Cuayáhuitl; Nina Dethlefs; Markus Endres; Amir-massoud Farahmand; Mark Fox; Lutz Frommberger; Sam Ganzfried; Yolanda Gil; et al. (AI Mag., 2015)

Heart Sound Classification Based on Temporal Alignment Techniques. - Jose Javier Gonzalez Ortiz; Cheng Perng Phoo; Jenna Wiens (CinC, 2016)

Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach. - Jenna Wiens; John V. Guttag; Eric Horvitz (J. Mach. Learn. Res., 2016)

Editorial: special issue on machine learning for health and medicine. - Jenna Wiens; Byron C. Wallace (Mach. Learn., 2016)

Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale. - Eli Sherman; Hitinder S. Gurm; Ulysses J. Balis; Scott R. Owens; Jenna Wiens (AMIA, 2017)

Contextual Motifs: Increasing the Utility of Motifs using Contextual Data. - Ian Fox; Lynn Ang; Mamta Jaiswal; Rodica Pop-Busui; Jenna Wiens (KDD, 2017)

Learning Credible Models. - Jiaxuan Wang; Jeeheh Oh; Jenna Wiens (CoRR, 2017)

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

Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories. - Ian Fox; Lynn Ang; Mamta Jaiswal; Rodica Pop-Busui; Jenna Wiens (KDD, 2018)

Learning Credible Models. - Jiaxuan Wang; Jeeheh Oh; Haozhu Wang; Jenna Wiens (KDD, 2018)

Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks. - Jeeheh Oh; Jiaxuan Wang; 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 Sekhar Sripada; Jenna Wiens (MLHC, 2018)

Clinically Meaningful Comparisons Over Time: An Approach to Measuring Patient Similarity based on Subsequence Alignment. - Dev Goyal; Zeeshan Syed; Jenna Wiens (CoRR, 2018)

The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA. - Jiaxuan Wang; Ian Fox; Jonathan Skaza; Nick Linck; Satinder Singh 001; Jenna Wiens (CoRR, 2018)

Advocacy Learning: Learning through Competition and Class-Conditional Representations. - Ian Fox; Jenna Wiens (IJCAI, 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, 2019)

Automatically Evaluating Balance: A Machine Learning Approach. - Tian Bao; Brooke N. Klatt; Susan L. Whitney; Kathleen H. Sienko; Jenna Wiens (CoRR, 2019)

The number needed to benefit: estimating the value of predictive analytics in healthcare. - Vincent X. Liu; David W. Bates; Jenna Wiens; Nigam H. Shah (J. Am. Medical Informatics Assoc., 2019)

Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction. - Harry Rubin-Falcone; Ian Fox; Jenna Wiens (KDH@ECAI, 2020)

Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. - Shengpu Tang; Aditya Modi 002; Michael W. Sjoding; Jenna Wiens (ICML, 2020)

Deep Reinforcement Learning for Closed-Loop Blood Glucose Control. - Ian Fox; Joyce M. Lee; Rodica Pop-Busui; 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)

AdaSGD: Bridging the gap between SGD and Adam. - Jiaxuan Wang; Jenna Wiens (CoRR, 2020)

Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. - Shengpu Tang; Parmida Davarmanesh; Yanmeng Song; Danai Koutra; Michael W. Sjoding; Jenna Wiens (J. Am. Medical Informatics Assoc., 2020)

Estimating Calibrated Individualized Survival Curves with Deep Learning. - Fahad Kamran; Jenna Wiens (AAAI, 2021)

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

Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. - Jiaxuan Wang; Jenna Wiens; Scott M. Lundberg (AISTATS, 2021)

Mind the Performance Gap: Examining Dataset Shift During Prospective Validation. - Erkin Ötles; 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 (MLHC, 2021)

Combining chest X-rays and EHR data using machine learning to diagnose acute respiratory failure. - Sarah Jabbour; David Fouhey; Ella Kazerooni; Jenna Wiens; Michael W. Sjoding (CoRR, 2021)

mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines. - Begüm D. Topçuoglu; Zena Lapp; Kelly L. Sovacool; Evan S. Snitkin; Jenna Wiens; Patrick D. Schloss (J. Open Source Softw., 2021)

Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units. - Jeremiah Hauth; Safa Jabri; Fahad Kamran; Eyoel W. Feleke; Kaleab Nigusie; Lauro V. Ojeda; Shirley Handelzalts; Linda Nyquist; Neil B. Alexander; Xun Huan; Jenna Wiens; Kathleen H. Sienko (Sensors, 2021)

Noninvasive Estimation of Hydration Status in Athletes Using Wearable Sensors and a Data-Driven Approach Based on Orthostatic Changes. - Fahad Kamran; Victor C. Le; Adam Frischknecht; Jenna Wiens; Kathleen H. Sienko (Sensors, 2021)

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

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

Learning Concept Credible Models for Mitigating Shortcuts. - Jiaxuan Wang; Sarah Jabbour; Maggie Makar; Michael W. Sjoding; Jenna Wiens (NeurIPS, 2022)

Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure. - Sarah Jabbour; David Fouhey; Ella Kazerooni; Jenna Wiens; Michael W. Sjoding (J. Am. Medical Informatics Assoc., 2022)

Automated Brain Masking of Fetal Functional MRI with Open Data. - Saige Rutherford; Pascal Sturmfels; Mike Angstadt; Jasmine L. Hect; Jenna Wiens; Marion I. van den Heuvel; Dustin Scheinost; Chandra Sekhar Sripada; Moriah E. Thomason (Neuroinformatics, 2022)

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

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

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

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

Leveraging Factored Action Spaces for Off-Policy Evaluation. - Aaman Rebello; Shengpu Tang; Jenna Wiens; Sonali Parbhoo (CoRR, 2023)

Learning to Rank for Optimal Treatment Allocation Under Resource Constraints. - Fahad Kamran; Maggie Makar; Jenna Wiens (AISTATS, 2024)

Multiple Instance Learning with Absolute Position Information. - Meera Krishnamoorthy; Jenna Wiens (CHIL, 2024)

DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks. - Sarah Jabbour; Gregory Kondas; Ella Kazerooni; Michael W. Sjoding; David Fouhey; Jenna Wiens (ECCV (64), 2024)

Automatically Classifying Vestibular Gait Using Time-series Data from Wearable IMUs. - Safa Jabri; Lucy Spicher; Wendy Carender; Jenna Wiens; Kathleen H. Sienko (EMBC, 2024)

Survival Analysis with Multiple Noisy Labels. - Donna Tjandra; Jenna Wiens (ICDM, 2024)

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

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

Learning control-ready forecasters for Blood Glucose Management. - Harry Rubin-Falcone; Joyce M. Lee; Jenna Wiens (Comput. Biol. Medicine, 2024)

CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation. - Aishwarya Mandyam; Shengpu Tang; Jiayu Yao; Jenna Wiens; Barbara E. Engelhardt (CoRR, 2024)

Shortcomings in the Evaluation of Blood Glucose Forecasting. - Jung Min Lee; Rodica Pop-Busui; Joyce M. Lee; Jesper Fleischer; Jenna Wiens (IEEE Trans. Biomed. Eng., 2024)

Understanding GNNs and Homophily in Dynamic Node Classification. - Michael Ito; Danai Koutra; Jenna Wiens (AISTATS, 2025)

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

Conditional Front-door Adjustment for Heterogeneous Treatment Assignment Effect Estimation Under Non-compliance. - Winston Chen; Trenton Chang; Jenna Wiens (CHIL, 2025)

AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. - Shalmali Joshi; Iñigo Urteaga; Wouter A. C. van Amsterdam; George Hripcsak; Pierre A. Elias; Benjamin R. C. Amor; Noémie Elhadad; James C. Fackler; Mark P. Sendak; Jenna Wiens; Kaivalya Deshpande; Yoav Wald; et al. (J. Am. Medical Informatics Assoc., 2025)

Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes resulting from disease onset: a case study in sepsis. - Fahad Kamran; Donna Tjandra; Thomas S. Valley; Hallie C. Prescott; Nigam H. Shah; Vincent X. Liu; Eric Horvitz; Jenna Wiens (J. Am. Medical Informatics Assoc., 2025)

Towards Fast Graph Generation via Autoregressive Noisy Filtration Modeling. - Markus Krimmel; Jenna Wiens; Karsten M. Borgwardt; Dexiong Chen (CoRR, 2025)

Conditional Front-door Adjustment for Heterogeneous Treatment Assignment Effect Estimation Under Non-adherence. - Winston Chen; Trenton Chang; Jenna Wiens (CoRR, 2025)

A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs. - Trenton Chang; Tobias Schnabel; Adith Swaminathan; Jenna Wiens (CoRR, 2025)

Estimating Misreporting in the Presence of Genuine Modification: A Causal Perspective. - Dylan Zapzalka; Trenton Chang; Lindsay A. Warrenburg; Sae-Hwan Park; Daniel K. Shenfeld; Ravi B. Parikh; Jenna Wiens; Maggie Makar (CoRR, 2025)

On the Limits of Selective AI Prediction: A Case Study in Clinical Decision Making. - Sarah Jabbour; David Fouhey; Nikola Banovic 001; Stephanie D. Shepard; Ella Kazerooni; Michael W. Sjoding; Jenna Wiens (CoRR, 2025)