Jenna Wiens - RankMe

109 papers A* 18A 4Misc 2Journal 56Unranked 22

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs. AAAI Trenton Chang, Tobias Schnabel, Adith Swaminathan, Jenna Wiens
2026	J	`jnl`	Fast Graph Generation via Autoregressive Noisy Filtration Modeling. Trans. Mach. Learn. Res. Markus Krimmel, Jenna Wiens, Karsten M. Borgwardt, Dexiong Chen
2026	A*	`conf`	Measuring Model Performance in the Presence of an Intervention. AAAI Winston Chen, Michael W. Sjoding, Jenna Wiens
2025	J	`jnl`	A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs. CoRR Trenton Chang, Tobias Schnabel, Adith Swaminathan, Jenna Wiens
2025	J	`jnl`	AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. J. Am. Medical Informatics Assoc. 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, Madalina Fiterau, Zachary C. Lipton, Daniel Malinsky, Madhur Nayan, Hongseok Namkoong, Soojin Park, Julia E. Vogt, Rajesh Ranganath
2025	J	`jnl`	Conditional Front-door Adjustment for Heterogeneous Treatment Assignment Effect Estimation Under Non-adherence. CoRR Winston Chen, Trenton Chang, Jenna Wiens
2025	—	`conf`	Conditional Front-door Adjustment for Heterogeneous Treatment Assignment Effect Estimation Under Non-compliance. CHIL Winston Chen, Trenton Chang, Jenna Wiens
2025	—	`conf`	Cross-Validation for Longitudinal Datasets with Unstable Correlations. KDD (2) Meera Krishnamoorthy, Michael W. Sjoding, Jenna Wiens
2025	J	`jnl`	Estimating Misreporting in the Presence of Genuine Modification: A Causal Perspective. CoRR Dylan Zapzalka, Trenton Chang, Lindsay A. Warrenburg, Sae-Hwan Park, Daniel K. Shenfeld, Ravi B. Parikh, Jenna Wiens, Maggie Makar
2025	A	`conf`	Learning Laplacian Positional Encodings for Heterophilous Graphs. AISTATS Michael Ito, Jiong Zhu, Dexiong Chen, Danai Koutra, Jenna Wiens
2025	J	`jnl`	Learning Laplacian Positional Encodings for Heterophilous Graphs. CoRR Michael Ito, Jiong Zhu, Dexiong Chen, Danai Koutra, Jenna Wiens
2025	J	`jnl`	Measuring Model Performance in the Presence of an Intervention. CoRR Winston Chen, Michael W. Sjoding, Jenna Wiens
2025	J	`jnl`	On the Limits of Selective AI Prediction: A Case Study in Clinical Decision Making. CoRR Sarah Jabbour, David Fouhey, Nikola Banovic, Stephanie D. Shepard, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens
2025	J	`jnl`	Random Search Neural Networks for Efficient and Expressive Graph Learning. CoRR Michael Ito, Danai Koutra, Jenna Wiens
2025	J	`jnl`	Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes resulting from disease onset: a case study in sepsis. J. Am. Medical Informatics Assoc. Fahad Kamran, Donna Tjandra, Thomas S. Valley, Hallie C. Prescott, Nigam H. Shah, Vincent X. Liu, Eric Horvitz, Jenna Wiens
2025	J	`jnl`	Towards Fast Graph Generation via Autoregressive Noisy Filtration Modeling. CoRR Markus Krimmel, Jenna Wiens, Karsten M. Borgwardt, Dexiong Chen
2025	A	`conf`	Understanding GNNs and Homophily in Dynamic Node Classification. AISTATS Michael Ito, Danai Koutra, Jenna Wiens
2025	J	`jnl`	Understanding GNNs and Homophily in Dynamic Node Classification. CoRR Michael Ito, Danai Koutra, Jenna Wiens
2024	—	`conf`	Automatically Classifying Vestibular Gait Using Time-series Data from Wearable IMUs. EMBC Safa Jabri, Lucy Spicher, Wendy Carender, Jenna Wiens, Kathleen H. Sienko
2024	J	`jnl`	CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation. CoRR Aishwarya Mandyam, Shengpu Tang, Jiayu Yao, Jenna Wiens, Barbara E. Engelhardt
2024	—	`conf`	DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks. ECCV (64) Sarah Jabbour, Gregory Kondas, Ella Kazerooni, Michael W. Sjoding, David Fouhey, Jenna Wiens
2024	J	`jnl`	DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks. CoRR Sarah Jabbour, Gregory Kondas, Ella Kazerooni, Michael W. Sjoding, David Fouhey, Jenna Wiens
2024	A*	`conf`	From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions. ICML Trenton Chang, Jenna Wiens
2024	J	`jnl`	From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions. CoRR Trenton Chang, Jenna Wiens
2024	J	`jnl`	Learning control-ready forecasters for Blood Glucose Management. Comput. Biol. Medicine Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens
2024	A	`conf`	Learning to Rank for Optimal Treatment Allocation Under Resource Constraints. AISTATS Fahad Kamran, Maggie Makar, Jenna Wiens
2024	—	`conf`	Multiple Instance Learning with Absolute Position Information. CHIL Meera Krishnamoorthy, Jenna Wiens
2024	J	`jnl`	Shortcomings in the Evaluation of Blood Glucose Forecasting. IEEE Trans. Biomed. Eng. Jung Min Lee, Rodica Pop-Busui, Joyce M. Lee, Jesper Fleischer, Jenna Wiens
2024	A*	`conf`	Survival Analysis with Multiple Noisy Labels. ICDM Donna Tjandra, Jenna Wiens
2024	A*	`conf`	Who's Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation. NeurIPS Trenton Chang, Lindsay A. Warrenburg, Sae-Hwan Park, Ravi B. Parikh, Maggie Makar, Jenna Wiens
2024	J	`jnl`	Who's Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation. CoRR Trenton Chang, Lindsay A. Warrenburg, Sae-Hwan Park, Ravi B. Parikh, Maggie Makar, Jenna Wiens
2023	A*	`conf`	Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation. NeurIPS Shengpu Tang, Jenna Wiens
2023	J	`jnl`	Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation. CoRR Shengpu Tang, Jenna Wiens
2023	—	`conf`	Denoising Autoencoders for Learning from Noisy Patient-Reported Data. CHIL Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens
2023	A*	`conf`	Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose. AAAI Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens
2023	J	`jnl`	Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose. CoRR Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens
2023	J	`jnl`	Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. CoRR Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens
2023	J	`jnl`	Leveraging Factored Action Spaces for Off-Policy Evaluation. CoRR Aaman Rebello, Shengpu Tang, Jenna Wiens, Sonali Parbhoo
2023	—	`conf`	Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise. CHIL Donna Tjandra, Jenna Wiens
2023	J	`jnl`	Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise. CoRR Donna Tjandra, Jenna Wiens
2023	—	`conf`	Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance. MLHC Erkin Ötles, Brian T. Denton, Jenna Wiens
2023	J	`jnl`	Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance. CoRR Erkin Ötles, Brian T. Denton, Jenna Wiens
2022	J	`jnl`	Automated Brain Masking of Fetal Functional MRI with Open Data. Neuroinformatics Saige Rutherford, Pascal Sturmfels, Mike Angstadt, Jasmine L. Hect, Jenna Wiens, Marion I. van den Heuvel, Dustin Scheinost, Chandra Sekhar Sripada, Moriah E. Thomason
2022	J	`jnl`	Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure. J. Am. Medical Informatics Assoc. Sarah Jabbour, David Fouhey, Ella Kazerooni, Jenna Wiens, Michael W. Sjoding
2022	—	`conf`	Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning. MLHC Trenton Chang, Michael W. Sjoding, Jenna Wiens
2022	J	`jnl`	Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning. CoRR Trenton Chang, Michael W. Sjoding, Jenna Wiens
2022	A*	`conf`	Learning Concept Credible Models for Mitigating Shortcuts. NeurIPS Jiaxuan Wang, Sarah Jabbour, Maggie Makar, Michael W. Sjoding, Jenna Wiens
2022	A*	`conf`	Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. NeurIPS Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens
2021	A*	`conf`	A Hierarchical Approach to Multi-Event Survival Analysis. AAAI Donna Tjandra, Yifei He, Jenna Wiens
2021	J	`jnl`	Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units. Sensors 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
2021	J	`jnl`	Combining chest X-rays and EHR data using machine learning to diagnose acute respiratory failure. CoRR Sarah Jabbour, David Fouhey, Ella Kazerooni, Jenna Wiens, Michael W. Sjoding
2021	A*	`conf`	Estimating Calibrated Individualized Survival Curves with Deep Learning. AAAI Fahad Kamran, Jenna Wiens
2021	—	`conf`	Mind the Performance Gap: Examining Dataset Shift During Prospective Validation. MLHC Erkin Ötles, Jeeheh Oh, Benjamin Li, Michelle Bochinski, Hyeon Joo, Justin Ortwine, Erica Shenoy, Laraine Washer, Vincent B. Young, Krishna Rao, Jenna Wiens
2021	J	`jnl`	Mind the Performance Gap: Examining Dataset Shift During Prospective Validation. CoRR Erkin Ötles, Jeeheh Oh, Benjamin Li, Michelle Bochinski, Hyeon Joo, Justin Ortwine, Erica Shenoy, Laraine Washer, Vincent B. Young, Krishna Rao, Jenna Wiens
2021	—	`conf`	Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings. MLHC Shengpu Tang, Jenna Wiens
2021	J	`jnl`	Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings. CoRR Shengpu Tang, Jenna Wiens
2021	J	`jnl`	Noninvasive Estimation of Hydration Status in Athletes Using Wearable Sensors and a Data-Driven Approach Based on Orthostatic Changes. Sensors Fahad Kamran, Victor C. Le, Adam Frischknecht, Jenna Wiens, Kathleen H. Sienko
2021	A	`conf`	Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. AISTATS Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg
2021	J	`jnl`	mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines. J. Open Source Softw. Begüm D. Topçuoglu, Zena Lapp, Kelly L. Sovacool, Evan S. Snitkin, Jenna Wiens, Patrick D. Schloss
2020	J	`jnl`	AdaSGD: Bridging the gap between SGD and Adam. CoRR Jiaxuan Wang, Jenna Wiens
2020	A*	`conf`	Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. ICML Shengpu Tang, Aditya Modi, Michael W. Sjoding, Jenna Wiens
2020	J	`jnl`	Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. CoRR Shengpu Tang, Aditya Modi, Michael W. Sjoding, Jenna Wiens
2020	—	`conf`	Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts. MLHC Sarah Jabbour, David Fouhey, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens
2020	J	`jnl`	Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts. CoRR Sarah Jabbour, David Fouhey, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens
2020	—	`conf`	Deep Reinforcement Learning for Closed-Loop Blood Glucose Control. MLHC Ian Fox, Joyce M. Lee, Rodica Pop-Busui, Jenna Wiens
2020	J	`jnl`	Deep Reinforcement Learning for Closed-Loop Blood Glucose Control. CoRR Ian Fox, Joyce M. Lee, Rodica Pop-Busui, Jenna Wiens
2020	—	`conf`	Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction. KDH@ECAI Harry Rubin-Falcone, Ian Fox, Jenna Wiens
2020	J	`jnl`	Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. J. Am. Medical Informatics Assoc. Shengpu Tang, Parmida Davarmanesh, Yanmeng Song, Danai Koutra, Michael W. Sjoding, Jenna Wiens
2020	—	`ed.`	Proceedings of the Machine Learning for Healthcare Conference, MLHC 2020, 7-8 August 2020, Virtual Event, Durham, NC, USA. MLHC Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens
2020	J	`jnl`	Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. CoRR Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg
2019	A*	`conf`	Advocacy Learning: Learning through Competition and Class-Conditional Representations. IJCAI Ian Fox, Jenna Wiens
2019	J	`jnl`	Advocacy Learning: Learning through Competition and Class-Conditional Representations. CoRR Ian Fox, Jenna Wiens
2019	J	`jnl`	Automatically Evaluating Balance: A Machine Learning Approach. CoRR Tian Bao, Brooke N. Klatt, Susan L. Whitney, Kathleen H. Sienko, Jenna Wiens
2019	—	`ed.`	Proceedings of the Machine Learning for Healthcare Conference, MLHC 2019, 9-10 August 2019, Ann Arbor, Michigan, USA. MLHC Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens
2019	—	`conf`	Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series. MLHC Jeeheh Oh, Jiaxuan Wang, Shengpu Tang, Michael W. Sjoding, Jenna Wiens
2019	J	`jnl`	Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series. CoRR Jeeheh Oh, Jiaxuan Wang, Shengpu Tang, Michael W. Sjoding, Jenna Wiens
2019	J	`jnl`	The number needed to benefit: estimating the value of predictive analytics in healthcare. J. Am. Medical Informatics Assoc. Vincent X. Liu, David W. Bates, Jenna Wiens, Nigam H. Shah
2018	—	`conf`	A Domain Guided CNN Architecture for Predicting Age from Structural Brain Images. MLHC Pascal Sturmfels, Saige Rutherford, Mike Angstadt, Mark Peterson, Chandra Sekhar Sripada, Jenna Wiens
2018	J	`jnl`	A Domain Guided CNN Architecture for Predicting Age from Structural Brain Images. CoRR Pascal Sturmfels, Saige Rutherford, Mike Angstadt, Mark Peterson, Chandra Sekhar Sripada, Jenna Wiens
2018	J	`jnl`	Clinically Meaningful Comparisons Over Time: An Approach to Measuring Patient Similarity based on Subsequence Alignment. CoRR Dev Goyal, Zeeshan Syed, Jenna Wiens
2018	A*	`conf`	Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories. KDD Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens
2018	J	`jnl`	Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories. CoRR Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens
2018	A*	`conf`	Learning Credible Models. KDD Jiaxuan Wang, Jeeheh Oh, Haozhu Wang, Jenna Wiens
2018	A*	`conf`	Learning the Probability of Activation in the Presence of Latent Spreaders. AAAI Maggie Makar, John V. Guttag, Jenna Wiens
2018	—	`conf`	Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks. MLHC Jeeheh Oh, Jiaxuan Wang, Jenna Wiens
2018	J	`jnl`	Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks. CoRR Jeeheh Oh, Jiaxuan Wang, Jenna Wiens
2018	J	`jnl`	Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale. CoRR Eli Sherman, Hitinder S. Gurm, Ulysses J. Balis, Scott R. Owens, Jenna Wiens
2018	—	`ed.`	Proceedings of the Machine Learning for Healthcare Conference, MLHC 2018, 17-18 August 2018, Palo Alto, California MLHC Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens
2018	J	`jnl`	The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA. CoRR Jiaxuan Wang, Ian Fox, Jonathan Skaza, Nick Linck, Satinder Singh, Jenna Wiens
2017	A*	`conf`	Contextual Motifs: Increasing the Utility of Motifs using Contextual Data. KDD Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens
2017	J	`jnl`	Contextual Motifs: Increasing the Utility of Motifs using Contextual Data. CoRR Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens
2017	J	`jnl`	Learning Credible Models. CoRR Jiaxuan Wang, Jeeheh Oh, Jenna Wiens
2017	J	`jnl`	Learning the Probability of Activation in the Presence of Latent Spreaders. CoRR Maggie Makar, John V. Guttag, Jenna Wiens
2017	Misc	`conf`	Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale. AMIA Eli Sherman, Hitinder S. Gurm, Ulysses J. Balis, Scott R. Owens, Jenna Wiens
2017	—	`ed.`	Proceedings of the Machine Learning for Healthcare Conference, MLHC 2017, Boston, Massachusetts, USA, 18-19 August 2017 MLHC Finale Doshi-Velez, Jim Fackler, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens
2016	J	`jnl`	Editorial: special issue on machine learning for health and medicine. Mach. Learn. Jenna Wiens, Byron C. Wallace
2016	—	`conf`	Heart Sound Classification Based on Temporal Alignment Techniques. CinC Jose Javier Gonzalez Ortiz, Cheng Perng Phoo, Jenna Wiens
2016	J	`jnl`	Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach. J. Mach. Learn. Res. Jenna Wiens, John V. Guttag, Eric Horvitz
2016	—	`ed.`	Proceedings of the 1st Machine Learning for Healthcare, MLHC 2016, Los Angeles, CA, USA, August 19-20, 2016 MLHC Finale Doshi-Velez, Jim Fackler, David C. Kale, Byron C. Wallace, Jenna Wiens
2015	A*	`conf`	Automated Feature Learning: Mining Unstructured Data for Useful Abstractions. ICDM Abhishek Bafna, Jenna Wiens
2015	Misc	`conf`	Learning Useful Abstractions from the Web. AMIA Abhishek Bafna, Jenna Wiens
2015	J	`jnl`	Reports of the AAAI 2014 Conference Workshops. AI Mag. 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
2014	J	`jnl`	A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions. J. Am. Medical Informatics Assoc. Jenna Wiens, John V. Guttag, Eric Horvitz
2014	—	`—`	Learning to prevent healthcare-associated infections: leveraging data across time and space to improve local predictions. Jenna Wiens
2014	—	`ed.`	Modern Artificial Intelligence for Health Analytics, Papers from the 2014 AAAI Workshop, Québec City, Québec, Canada, July 27, 2014 AAAI Workshop: Modern Artificial Intelligence for Health Analytics Finale Doshi-Velez, David C. Kale, Byron C. Wallace, Jenna Wiens
2014	—	`conf`	Preface. AAAI Workshop: Modern Artificial Intelligence for Health Analytics Finale Doshi-Velez, David C. Kale, Byron C. Wallace, Jenna Wiens
2012	—	`conf`	Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task. NIPS Jenna Wiens, John V. Guttag, Eric Horvitz
2011	—	`conf`	Patient-specific ventricular beat classification without patient-specific expert knowledge: A transfer learning approach. EMBC Jenna Wiens, John V. Guttag
2010	—	`conf`	Active Learning Applied to Patient-Adaptive Heartbeat Classification. NIPS Jenna Wiens, John V. Guttag