Thomas Hartvigsen

According to our database, Thomas Hartvigsen authored at least 41 papers between 2017 and 2024.

Collaborative distances:

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

2024

UniTS: Building a Unified Time Series Model.

Shanghua Gao, Teddy Koker, Owen Queen, Thomas Hartvigsen, Theodoros Tsiligkaridis, Marinka Zitnik

CoRR, 2024

MATHWELL: Generating Educational Math Word Problems at Scale.

Bryan R. Christ, Jonathan Kropko, Thomas Hartvigsen

CoRR, 2024

Improving Black-box Robustness with In-Context Rewriting.

Kyle O'Brien, Nathan Ng, Isha Puri, Jorge Mendez, Hamid Palangi, Yoon Kim, Marzyeh Ghassemi, Thomas Hartvigsen

CoRR, 2024

Learning from Time Series under Temporal Label Noise.

Sujay Nagaraj, Walter Gerych, Sana Tonekaboni, Anna Goldenberg, Berk Ustun, Thomas Hartvigsen

CoRR, 2024

2023

Machine Learning for Health symposium 2023 - Findings track.

Stefan Hegselmann, Antonio Parziale, Divya Shanmugam, Shengpu Tang, Mercy Nyamewaa Asiedu, Serina Chang, Thomas Hartvigsen, Harvineet Singh

CoRR, 2023

Continuous Time Evidential Distributions for Irregular Time Series.

Taylor W. Killian, Haoran Zhang, Thomas Hartvigsen, Ava P. Amini

CoRR, 2023

Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency.

Owen Queen, Thomas Hartvigsen, Teddy Koker, Huan He, Theodoros Tsiligkaridis, Marinka Zitnik

CoRR, 2023

Interpretable Unified Language Checking.

Tianhua Zhang, Hongyin Luo, Yung-Sung Chuang, Wei Fang, Luc Gaitskell, Thomas Hartvigsen, Xixin Wu, Danny Fox, Helen Meng, James R. Glass

CoRR, 2023

Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks.

Thomas Hartvigsen, Jidapa Thadajarassiri, Xiangnan Kong, Elke A. Rundensteiner

CoRR, 2023

Machine Learning for Health (ML4H) 2023.

Stefan Hegselmann, Antonio Parziale, Divya Shanmugam, Shengpu Tang, Kristen Severson, Mercy Nyamewaa Asiedu, Serina Chang, Bonaventure F. P. Dossou, Qian Huang, Fahad Kamran, Haoran Zhang, Sujay Nagaraj, Luis Oala, Shan Xu, Chinasa T. Okolo, Helen Zhou, Jessica Dafflon, Caleb Ellington, Sarah Jabbour, Hyewon Jeong, Harry Reyes Nieva, Yuzhe Yang, Ghada Zamzmi, Vishwali Mhasawade, Van Truong, Payal Chandak, Matthew Lee, Peniel Argaw, Kyle Heuton, Harvineet Singh, Thomas Hartvigsen

Proceedings of the Machine Learning for Health, 2023

Taking Off with AI: Lessons from Aviation for Healthcare.

Elizabeth Bondi-Kelly, Thomas Hartvigsen, Lindsay M. Sanneman, Swami Sankaranarayanan, Zach Harned, Grace Wickerson, Judy Wawira Gichoya, Lauren Oakden-Rayner, Leo Anthony Celi, Matthew P. Lungren, Julie A. Shah, Marzyeh Ghassemi

Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, 2023

Multi-State Brain Network Discovery.

Hang Yin, Yao Su, Xinyue Liu, Thomas Hartvigsen, Yanhua Li, Xiangnan Kong

Proceedings of the IEEE International Conference on Big Data, 2023

Stabilizing Adversarial Training for Generative Networks.

Walter Gerych, Kevin Hickey, Thomas Hartvigsen, Luke Buquicchio, Abdulaziz Alajaji, Kavin Chandrasekaran, Hamid Mansoor, Emmanuel Agu, Elke A. Rundensteiner

Proceedings of the IEEE International Conference on Big Data, 2023

Knowledge Amalgamation for Multi-Label Classification via Label Dependency Transfer.

Jidapa Thadajarassiri, Thomas Hartvigsen, Walter Gerych, Xiangnan Kong, Elke A. Rundensteiner

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

2022

Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors.

Thomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, Marzyeh Ghassemi

CoRR, 2022

Positive Unlabeled Learning with a Sequential Selection Bias.

Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Abdulaziz Alajaji, Kavin Chandrasekaran, Hamid Mansoor, Elke A. Rundensteiner, Emmanuel Agu

Proceedings of the 2022 SIAM International Conference on Data Mining, 2022

TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks.

Ruofan Hu, Dongyu Zhang, Dandan Tao, Thomas Hartvigsen, Hao Feng, Elke A. Rundensteiner

Proceedings of the Thirteenth Language Resources and Evaluation Conference, 2022

Class-Specific Explainability for Deep Time Series Classifiers.

Ramesh Doddaiah, Prathyush S. Parvatharaju, Elke A. Rundensteiner, Thomas Hartvigsen

Proceedings of the IEEE International Conference on Data Mining, 2022

The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations.

Aparna Balagopalan, Haoran Zhang, Kimia Hamidieh, Thomas Hartvigsen, Frank Rudzicz, Marzyeh Ghassemi

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Stop&Hop: Early Classification of Irregular Time Series.

Thomas Hartvigsen, Walter Gerych, Jidapa Thadajarassiri, Xiangnan Kong, Elke A. Rundensteiner

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

Robust Recurrent Classifier Chains for Multi-Label Learning with Missing Labels.

Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Emmanuel Agu, Elke A. Rundensteiner

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection.

Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, Ece Kamar

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

Recovering the Propensity Score from Biased Positive Unlabeled Data.

Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Emmanuel Agu, Elke A. Rundensteiner

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Recurrent Bayesian Classifier Chains for Exact Multi-Label Classification.

Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Emmanuel Agu, Elke A. Rundensteiner

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Energy-Efficient Models for High-Dimensional Spike Train Classification using Sparse Spiking Neural Networks.

Hang Yin, John Boaz Lee, Xiangnan Kong, Thomas Hartvigsen, Sihong Xie

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Learning Saliency Maps to Explain Deep Time Series Classifiers.

Prathyush S. Parvatharaju, Ramesh Doddaiah, Thomas Hartvigsen, Elke A. Rundensteiner

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

Human-like Explanation for Text Classification With Limited Attention Supervision.

Dongyu Zhang, Cansu Sen, Jidapa Thadajarassiri, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Variational Open Set Recognition (VOSR).

Luke Buquicchio, Walter Gerych, Abdulaziz Alajaji, Kavin Chandrasekaran, Hamid Mansoor, Thomas Hartvigsen, Elke A. Rundensteiner, Emmanuel Agu

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Semi-Supervised Knowledge Amalgamation for Sequence Classification.

Jidapa Thadajarassiri, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner

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

2020

Recurrent Halting Chain for Early Multi-label Classification.

Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke A. Rundensteiner

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Learning to Selectively Update State Neurons in Recurrent Networks.

Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke A. Rundensteiner

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

Clinical Performance Evaluation of a Machine Learning System for Predicting Hospital-Acquired Clostridium Difficile Infection.

Erin Teeple, Thomas Hartvigsen, Cansu Sen, Kajal T. Claypool, Elke A. Rundensteiner

Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020), 2020

Learning Similarity-Preserving Meta-Embedding for Text Mining.

Jidapa Thadajarassiri, Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words?

Cansu Sen, Thomas Hartvigsen, Biao Yin, Xiangnan Kong, Elke A. Rundensteiner

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019

Adaptive-Halting Policy Network for Early Classification.

Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke A. Rundensteiner

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Learning Temporal Relevance in Longitudinal Medical Notes.

Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Patient-level Classification on Clinical Note Sequences Guided by Attributed Hierarchical Attention.

Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Comparing General and Locally-Learned Word Embeddings for Clinical Text Mining.

Jidapa Thadajarassiri, Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner

Proceedings of the 2019 IEEE EMBS International Conference on Biomedical & Health Informatics, 2019

2018

Detecting MRSA Infections by Fusing Structured and Unstructured Electronic Health Record Data.

Thomas Hartvigsen, Cansu Sen, Elke A. Rundensteiner

Proceedings of the Biomedical Engineering Systems and Technologies, 2018

Early Prediction of MRSA Infections using Electronic Health Records.

Thomas Hartvigsen, Cansu Sen, Sarah Brownell, Erin Teeple, Xiangnan Kong, Elke A. Rundensteiner

Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018), 2018

2017

CREST - Risk Prediction for Clostridium Difficile Infection Using Multimodal Data Mining.

Cansu Sen, Thomas Hartvigsen, Elke A. Rundensteiner, Kajal T. Claypool

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017