Jenna Wiens

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 70

Top Venues

CoRR

MLHC

J. Am. Medical Informatics Assoc.

AAAI

Massachusetts Institute of Technology, Cambridge, MA, USA (PHD)

Education History

Massachusetts Institute of Technology, Cambridge, MA, USA

PHD

Publications

Trenton Chang, Jenna Wiens
From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions. CoRR (2024)
Fahad Kamran, Maggie Makar, Jenna Wiens
Learning to Rank for Optimal Treatment Allocation Under Resource Constraints. AISTATS (2024)
Sarah Jabbour, Gregory Kondas, Ella Kazerooni, Michael W. Sjoding, David Fouhey, Jenna Wiens
DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks. CoRR (2024)
Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens
Learning control-ready forecasters for Blood Glucose Management. Comput. Biol. Medicine 180 (2024)
Donna Tjandra, Jenna Wiens
Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise. CoRR (2023)
Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. CoRR (2023)
Shengpu Tang, Jenna Wiens
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation. NeurIPS (2023)
Erkin Ötles, Brian T. Denton, Jenna Wiens
Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance. CoRR (2023)
Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens
Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose. AAAI (2023)
Harry Rubin-Falcone, Joyce Lee, Jenna Wiens
Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose. CoRR (2023)
Erkin Ötles, Brian T. Denton, Jenna Wiens
Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance. MLHC (2023)
Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens
Denoising Autoencoders for Learning from Noisy Patient-Reported Data. CHIL (2023)
Donna Tjandra, Jenna Wiens
Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise. CHIL (2023)
Aaman Rebello, Shengpu Tang, Jenna Wiens, Sonali Parbhoo
Leveraging Factored Action Spaces for Off-Policy Evaluation. CoRR (2023)
Shengpu Tang, Jenna Wiens
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation. CoRR (2023)
Trenton Chang, Michael W. Sjoding, Jenna Wiens
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning. CoRR (2022)
Sarah Jabbour, David Fouhey, Ella Kazerooni, Jenna Wiens, Michael W. Sjoding
Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure. J. Am. Medical Informatics Assoc. 29 (6) (2022)
Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. NeurIPS (2022)
Trenton Chang, Michael W. Sjoding, Jenna Wiens
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning. MLHC (2022)
Jiaxuan Wang, Sarah Jabbour, Maggie Makar, Michael W. Sjoding, Jenna Wiens
Learning Concept Credible Models for Mitigating Shortcuts. NeurIPS (2022)
Saige Rutherford, Pascal Sturmfels, Mike Angstadt, Jasmine L. Hect, Jenna Wiens, Marion I. van den Heuvel, Dustin Scheinost, Chandra Sripada, Moriah E. Thomason
Automated Brain Masking of Fetal Functional MRI with Open Data. Neuroinformatics 20 (1) (2022)
Erkin Ötles, Jeeheh Oh, Benjamin Li, Michelle Bochinski, Hyeon Joo, Justin Ortwine, Erica Shenoy, Laraine Washer, Vincent B. Young, Krishna Rao, Jenna Wiens
Mind the Performance Gap: Examining Dataset Shift During Prospective Validation. CoRR (2021)
Jeremiah Hauth, Safa Jabri, Fahad Kamran, Eyoel W. Feleke, Kaleab Nigusie, Lauro Ojeda, Shirley Handelzalts, Linda Nyquist, Neil B. Alexander, Xun Huan, Jenna Wiens, Kathleen H. Sienko
Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units. Sensors 21 (14) (2021)
Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. AISTATS (2021)
Shengpu Tang, Jenna Wiens
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings. MLHC (2021)
Fahad Kamran, Jenna Wiens
Estimating Calibrated Individualized Survival Curves with Deep Learning. AAAI (2021)
Donna Tjandra, Yifei He, Jenna Wiens
A Hierarchical Approach to Multi-Event Survival Analysis. AAAI (2021)
Shengpu Tang, Jenna Wiens
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings. CoRR (2021)
Fahad Kamran, Victor C. Le, Adam Frischknecht, Jenna Wiens, Kathleen H. Sienko
Noninvasive Estimation of Hydration Status in Athletes Using Wearable Sensors and a Data-Driven Approach Based on Orthostatic Changes. Sensors 21 (13) (2021)
Erkin Ötles, Jeeheh Oh, Benjamin Li, Michelle Bochinski, Hyeon Joo, Justin Ortwine, Erica Shenoy, Laraine Washer, Vincent B. Young, Krishna Rao, Jenna Wiens
Mind the Performance Gap: Examining Dataset Shift During Prospective Validation. MLHC (2021)
Begüm D. Topçuoglu, Zena Lapp, Kelly L. Sovacool, Evan S. Snitkin, Jenna Wiens, Patrick D. Schloss
mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines. J. Open Source Softw. 6 (61) (2021)
Sarah Jabbour, David Fouhey, Ella Kazerooni, Jenna Wiens, Michael W. Sjoding
Combining chest X-rays and EHR data using machine learning to diagnose acute respiratory failure. CoRR (2021)
Shengpu Tang, Aditya Modi, Michael W. Sjoding, Jenna Wiens
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. ICML (2020)
Harry Rubin-Falcone, Ian Fox, Jenna Wiens
Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction. KDH@ECAI (2020)
Sarah Jabbour, David Fouhey, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts. MLHC (2020)
Shengpu Tang, Parmida Davarmanesh, Yanmeng Song, Danai Koutra, Michael W. Sjoding, Jenna Wiens
Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. J. Am. Medical Informatics Assoc. 27 (12) (2020)
Sarah Jabbour, David Fouhey, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts. CoRR (2020)
Ian Fox, Joyce Lee, Rodica Pop-Busui, Jenna Wiens
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control. CoRR (2020)
Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. CoRR (2020)
Jiaxuan Wang, Jenna Wiens
AdaSGD: Bridging the gap between SGD and Adam. CoRR (2020)
Shengpu Tang, Aditya Modi, Michael W. Sjoding, Jenna Wiens
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. CoRR (2020)
Ian Fox, Joyce Lee, Rodica Pop-Busui, Jenna Wiens
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control. MLHC (2020)
Tian Bao, Brooke N. Klatt, Susan L. Whitney, Kathleen H. Sienko, Jenna Wiens
Automatically Evaluating Balance: A Machine Learning Approach. CoRR (2019)
Ian Fox, Jenna Wiens
Advocacy Learning: Learning through Competition and Class-Conditional Representations. IJCAI (2019)
Ian Fox, Jenna Wiens
Advocacy Learning: Learning through Competition and Class-Conditional Representations. CoRR (2019)
Jeeheh Oh, Jiaxuan Wang, Shengpu Tang, Michael W. Sjoding, Jenna Wiens
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series. MLHC (2019)
Vincent X. Liu, David W. Bates, Jenna Wiens, Nigam H. Shah
The number needed to benefit: estimating the value of predictive analytics in healthcare. J. Am. Medical Informatics Assoc. 26 (12) (2019)
Jeeheh Oh, Jiaxuan Wang, Shengpu Tang, Michael W. Sjoding, Jenna Wiens
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series. CoRR (2019)
Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens
Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories. KDD (2018)
Jeeheh Oh, Jiaxuan Wang, Jenna Wiens
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks. MLHC (2018)
Pascal Sturmfels, Saige Rutherford, Mike Angstadt, Mark Peterson, Chandra Sekhar Sripada, Jenna Wiens
A Domain Guided CNN Architecture for Predicting Age from Structural Brain Images. MLHC (2018)
Jiaxuan Wang, Ian Fox, Jonathan Skaza, Nick Linck, Satinder Singh, Jenna Wiens
The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA. CoRR (2018)
Pascal Sturmfels, Saige Rutherford, Mike Angstadt, Mark Peterson, Chandra Sekhar Sripada, Jenna Wiens
A Domain Guided CNN Architecture for Predicting Age from Structural Brain Images. CoRR (2018)
Jeeheh Oh, Jiaxuan Wang, Jenna Wiens
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks. CoRR (2018)
Jiaxuan Wang, Jeeheh Oh, Haozhu Wang, Jenna Wiens
Learning Credible Models. KDD (2018)
Eli Sherman, Hitinder S. Gurm, Ulysses J. Balis, Scott R. Owens, Jenna Wiens
Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale. CoRR (2018)
Dev Goyal, Zeeshan Syed, Jenna Wiens
Clinically Meaningful Comparisons Over Time: An Approach to Measuring Patient Similarity based on Subsequence Alignment. CoRR (2018)
Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens
Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories. CoRR (2018)
Maggie Makar, John V. Guttag, Jenna Wiens
Learning the Probability of Activation in the Presence of Latent Spreaders. AAAI (2018)
Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens
Contextual Motifs: Increasing the Utility of Motifs using Contextual Data. CoRR (2017)
Eli Sherman, Hitinder S. Gurm, Ulysses J. Balis, Scott R. Owens, Jenna Wiens
Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale. AMIA (2017)
Jiaxuan Wang, Jeeheh Oh, Jenna Wiens
Learning Credible Models. CoRR (2017)
Maggie Makar, John V. Guttag, Jenna Wiens
Learning the Probability of Activation in the Presence of Latent Spreaders. CoRR (2017)
Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens
Contextual Motifs: Increasing the Utility of Motifs using Contextual Data. KDD (2017)
Jenna Wiens, John V. Guttag, Eric Horvitz
Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach. J. Mach. Learn. Res. 17 (2016)
Jenna Wiens, Byron C. Wallace
Editorial: special issue on machine learning for health and medicine. Mach. Learn. 102 (3) (2016)
Jose Javier Gonzalez Ortiz, Cheng Perng Phoo, Jenna Wiens
Heart Sound Classification Based on Temporal Alignment Techniques. CinC (2016)
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, Sébastien Guillet, Lawrence E. Hunter, Arnav Jhala, Kristian Kersting, George Dimitri Konidaris, Freddy Lécué, Sheila A. McIlraith, Sriraam Natarajan, Zeinab Noorian, David Poole, Rémi Ronfard, Alessandro Saffiotti, Arash Shaban-Nejad, Biplav Srivastava, Gerald Tesauro, Rosario Uceda-Sosa, Guy Van den Broeck, Martijn van Otterlo, Byron C. Wallace, Paul Weng, Jenna Wiens, Jie Zhang
Reports of the AAAI 2014 Conference Workshops. AI Mag. 36 (1) (2015)
Abhishek Bafna, Jenna Wiens
Automated Feature Learning: Mining Unstructured Data for Useful Abstractions. ICDM (2015)
Abhishek Bafna, Jenna Wiens
Learning Useful Abstractions from the Web. AMIA (2015)
Finale Doshi-Velez, David C. Kale, Byron C. Wallace, Jenna Wiens
Preface. AAAI Workshop: Modern Artificial Intelligence for Health Analytics (2014)
Jenna Wiens, John V. Guttag, Eric Horvitz
A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions. J. Am. Medical Informatics Assoc. 21 (4) (2014)
Jenna Wiens, John V. Guttag, Eric Horvitz
Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task. NIPS (2012)
Jenna Wiens, John V. Guttag
Patient-specific ventricular beat classification without patient-specific expert knowledge: A transfer learning approach. EMBC (2011)
Jenna Wiens, John V. Guttag
Active Learning Applied to Patient-Adaptive Heartbeat Classification. NIPS (2010)

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Publication Activity (10 Years)

Top Topics

Top Venues

Jenna Wiens