Jenna Wiens

According to our database, Jenna Wiens authored at least 56 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Learning control-ready forecasters for Blood Glucose Management.

Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens

Comput. Biol. Medicine, 2024

From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions.

Trenton Chang, Jenna Wiens

Proceedings of the Forty-first International Conference on Machine Learning, 2024

DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks.

Sarah Jabbour, Gregory Kondas, Ella Kazerooni, Michael W. Sjoding, David Fouhey, Jenna Wiens

Proceedings of the Computer Vision - ECCV 2024, 2024

Learning to Rank for Optimal Treatment Allocation Under Resource Constraints.

Fahad Kamran, Maggie Makar, Jenna Wiens

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Leveraging Factored Action Spaces for Off-Policy Evaluation.

Aaman Rebello, Shengpu Tang, Jenna Wiens, Sonali Parbhoo

CoRR, 2023

Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation.

Shengpu Tang, Jenna Wiens

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Machine Learning for Healthcare Conference, 2023

Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise.

Donna Tjandra, Jenna Wiens

Proceedings of the Conference on Health, Inference, and Learning, 2023

Denoising Autoencoders for Learning from Noisy Patient-Reported Data.

Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens

Proceedings of the Conference on Health, Inference, and Learning, 2023

Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose.

Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

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

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

Learning Concept Credible Models for Mitigating Shortcuts.

Jiaxuan Wang, Sarah Jabbour, Maggie Makar, Michael W. Sjoding, Jenna Wiens

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare.

Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning.

Trenton Chang, Michael W. Sjoding, Jenna Wiens

Proceedings of the Machine Learning for Healthcare Conference, 2022

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

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

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

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

Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings.

Shengpu Tang, Jenna Wiens

Proceedings of the Machine Learning for Healthcare Conference, 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

Proceedings of the Machine Learning for Healthcare Conference, 2021

Shapley Flow: A Graph-based Approach to Interpreting Model Predictions.

Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

A Hierarchical Approach to Multi-Event Survival Analysis.

Donna Tjandra, Yifei He, Jenna Wiens

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

Estimating Calibrated Individualized Survival Curves with Deep Learning.

Fahad Kamran, Jenna Wiens

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

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

AdaSGD: Bridging the gap between SGD and Adam.

Jiaxuan Wang, Jenna Wiens

CoRR, 2020

Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts.

Sarah Jabbour, David Fouhey, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens

Proceedings of the Machine Learning for Healthcare Conference, 2020

Deep Reinforcement Learning for Closed-Loop Blood Glucose Control.

Ian Fox, Joyce M. Lee, Rodica Pop-Busui, Jenna Wiens

Proceedings of the Machine Learning for Healthcare Conference, 2020

Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies.

Shengpu Tang, Aditya Modi, Michael W. Sjoding, Jenna Wiens

Proceedings of the 37th International Conference on Machine Learning, 2020

Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction.

Harry Rubin-Falcone, Ian Fox, Jenna Wiens

Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data co-located with 24th European Conference on Artificial Intelligence, 2020

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

Automatically Evaluating Balance: A Machine Learning Approach.

Tian Bao, Brooke N. Klatt, Susan L. Whitney, Kathleen H. Sienko, Jenna Wiens

CoRR, 2019

Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series.

Jeeheh Oh, Jiaxuan Wang, Shengpu Tang, Michael W. Sjoding, Jenna Wiens

Proceedings of the Machine Learning for Healthcare Conference, 2019

Advocacy Learning: Learning through Competition and Class-Conditional Representations.

Ian Fox, Jenna Wiens

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

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, Jenna Wiens

CoRR, 2018

Clinically Meaningful Comparisons Over Time: An Approach to Measuring Patient Similarity based on Subsequence Alignment.

Dev Goyal, Zeeshan Syed, Jenna Wiens

CoRR, 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

Proceedings of the Machine Learning for Healthcare Conference, 2018

Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks.

Jeeheh Oh, Jiaxuan Wang, Jenna Wiens

Proceedings of the Machine Learning for Healthcare Conference, 2018

Learning Credible Models.

Jiaxuan Wang, Jeeheh Oh, Haozhu Wang, Jenna Wiens

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories.

Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Learning the Probability of Activation in the Presence of Latent Spreaders.

Maggie Makar, John V. Guttag, Jenna Wiens

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Learning Credible Models.

Jiaxuan Wang, Jeeheh Oh, Jenna Wiens

CoRR, 2017

Contextual Motifs: Increasing the Utility of Motifs using Contextual Data.

Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale.

Eli Sherman, Hitinder S. Gurm, Ulysses J. Balis, Scott R. Owens, Jenna Wiens

Proceedings of the AMIA 2017, 2017

2016

Editorial: special issue on machine learning for health and medicine.

Jenna Wiens, Byron C. Wallace

Mach. Learn., 2016

Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach.

Jenna Wiens, John V. Guttag, Eric Horvitz

J. Mach. Learn. Res., 2016

Heart Sound Classification Based on Temporal Alignment Techniques.

Jose Javier Gonzalez Ortiz, Cheng Perng Phoo, Jenna Wiens

Proceedings of the Computing in Cardiology, CinC 2016, Vancouver, 2016

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, 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

AI Mag., 2015

Automated Feature Learning: Mining Unstructured Data for Useful Abstractions.

Abhishek Bafna, Jenna Wiens

Proceedings of the 2015 IEEE International Conference on Data Mining, 2015

Learning Useful Abstractions from the Web.

Abhishek Bafna, Jenna Wiens

Proceedings of the AMIA 2015, 2015

2014

Learning to prevent healthcare-associated infections: leveraging data across time and space to improve local predictions.

Jenna Wiens

PhD thesis, 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

Preface.

Finale Doshi-Velez, David C. Kale, Byron C. Wallace, Jenna Wiens

Proceedings of the Modern Artificial Intelligence for Health Analytics, 2014

2012

Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task.

Jenna Wiens, John V. Guttag, Eric Horvitz

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Patient-specific ventricular beat classification without patient-specific expert knowledge: A transfer learning approach.

Jenna Wiens, John V. Guttag

Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011

2010

Active Learning Applied to Patient-Adaptive Heartbeat Classification.

Jenna Wiens, John V. Guttag

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010