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Thomas Hartvigsen
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2020 – today
- 2024
- [i17]Sujay Nagaraj, Walter Gerych, Sana Tonekaboni, Anna Goldenberg, Berk Ustun, Thomas Hartvigsen:
Learning from Time Series under Temporal Label Noise. CoRR abs/2402.04398 (2024) - [i16]Kyle O'Brien, Nathan Ng, Isha Puri, Jorge Mendez, Hamid Palangi, Yoon Kim, Marzyeh Ghassemi, Thomas Hartvigsen:
Improving Black-box Robustness with In-Context Rewriting. CoRR abs/2402.08225 (2024) - [i15]Bryan R. Christ, Jonathan Kropko, Thomas Hartvigsen:
MATHWELL: Generating Educational Math Word Problems at Scale. CoRR abs/2402.15861 (2024) - [i14]Shanghua Gao, Teddy Koker, Owen Queen, Thomas Hartvigsen, Theodoros Tsiligkaridis, Marinka Zitnik:
UniTS: Building a Unified Time Series Model. CoRR abs/2403.00131 (2024) - [i13]Derek Powell, Walter Gerych, Thomas Hartvigsen:
TAXI: Evaluating Categorical Knowledge Editing for Language Models. CoRR abs/2404.15004 (2024) - 2023
- [c31]Jidapa Thadajarassiri, Thomas Hartvigsen, Walter Gerych, Xiangnan Kong, Elke A. Rundensteiner:
Knowledge Amalgamation for Multi-Label Classification via Label Dependency Transfer. AAAI 2023: 9980-9988 - [c30]Hang Yin, Yao Su, Xinyue Liu, Thomas Hartvigsen, Yanhua Li, Xiangnan Kong:
Multi-State Brain Network Discovery. IEEE Big Data 2023: 453-462 - [c29]Walter Gerych, Kevin Hickey, Thomas Hartvigsen, Luke Buquicchio, Abdulaziz Alajaji, Kavin Chandrasekaran, Hamid Mansoor, Emmanuel Agu, Elke A. Rundensteiner:
Stabilizing Adversarial Training for Generative Networks. IEEE Big Data 2023: 5223-5232 - [c28]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:
Taking Off with AI: Lessons from Aviation for Healthcare. EAAMO 2023: 4:1-4:14 - [c27]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:
Machine Learning for Health (ML4H) 2023. ML4H@NeurIPS 2023: 1-12 - [i12]Thomas Hartvigsen, Jidapa Thadajarassiri, Xiangnan Kong, Elke A. Rundensteiner:
Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks. CoRR abs/2302.04052 (2023) - [i11]Tianhua Zhang, Hongyin Luo, Yung-Sung Chuang, Wei Fang, Luc Gaitskell, Thomas Hartvigsen, Xixin Wu, Danny Fox, Helen Meng, James R. Glass:
Interpretable Unified Language Checking. CoRR abs/2304.03728 (2023) - [i10]Owen Queen, Thomas Hartvigsen, Teddy Koker, Huan He, Theodoros Tsiligkaridis, Marinka Zitnik:
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency. CoRR abs/2306.02109 (2023) - [i9]Taylor W. Killian, Haoran Zhang, Thomas Hartvigsen, Ava P. Amini:
Continuous Time Evidential Distributions for Irregular Time Series. CoRR abs/2307.13503 (2023) - [i8]Hang Yin, Yao Su, Xinyue Liu, Thomas Hartvigsen, Yanhua Li, Xiangnan Kong:
Multi-State Brain Network Discovery. CoRR abs/2311.02466 (2023) - [i7]Stefan Hegselmann, Antonio Parziale, Divya Shanmugam, Shengpu Tang, Mercy Nyamewaa Asiedu, Serina Chang, Thomas Hartvigsen, Harvineet Singh:
Machine Learning for Health symposium 2023 - Findings track. CoRR abs/2312.00655 (2023) - 2022
- [c26]Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Emmanuel Agu, Elke A. Rundensteiner:
Recovering the Propensity Score from Biased Positive Unlabeled Data. AAAI 2022: 6694-6702 - [c25]Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, Ece Kamar:
ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection. ACL (1) 2022: 3309-3326 - [c24]Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Emmanuel Agu, Elke A. Rundensteiner:
Robust Recurrent Classifier Chains for Multi-Label Learning with Missing Labels. CIKM 2022: 582-591 - [c23]Thomas Hartvigsen, Walter Gerych, Jidapa Thadajarassiri, Xiangnan Kong, Elke A. Rundensteiner:
Stop&Hop: Early Classification of Irregular Time Series. CIKM 2022: 696-705 - [c22]Aparna Balagopalan, Haoran Zhang, Kimia Hamidieh, Thomas Hartvigsen, Frank Rudzicz, Marzyeh Ghassemi:
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations. FAccT 2022: 1194-1206 - [c21]Ramesh Doddaiah, Prathyush S. Parvatharaju, Elke A. Rundensteiner, Thomas Hartvigsen:
Class-Specific Explainability for Deep Time Series Classifiers. ICDM 2022: 101-110 - [c20]Ruofan Hu, Dongyu Zhang, Dandan Tao, Thomas Hartvigsen, Hao Feng, Elke A. Rundensteiner:
TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks. LREC 2022: 6212-6222 - [c19]Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Abdulaziz Alajaji, Kavin Chandrasekaran, Hamid Mansoor, Elke A. Rundensteiner, Emmanuel Agu:
Positive Unlabeled Learning with a Sequential Selection Bias. SDM 2022: 19-27 - [i6]Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, Ece Kamar:
ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection. CoRR abs/2203.09509 (2022) - [i5]Aparna Balagopalan, Haoran Zhang, Kimia Hamidieh, Thomas Hartvigsen, Frank Rudzicz, Marzyeh Ghassemi:
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations. CoRR abs/2205.03295 (2022) - [i4]Ruofan Hu, Dongyu Zhang, Dandan Tao, Thomas Hartvigsen, Hao Feng, Elke A. Rundensteiner:
TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks. CoRR abs/2205.10726 (2022) - [i3]Thomas Hartvigsen, Walter Gerych, Jidapa Thadajarassiri, Xiangnan Kong, Elke A. Rundensteiner:
Stop&Hop: Early Classification of Irregular Time Series. CoRR abs/2208.09795 (2022) - [i2]Ramesh Doddaiah, Prathyush S. Parvatharaju, Elke A. Rundensteiner, Thomas Hartvigsen:
Class-Specific Explainability for Deep Time Series Classifiers. CoRR abs/2210.05411 (2022) - [i1]Thomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, Marzyeh Ghassemi:
Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors. CoRR abs/2211.11031 (2022) - 2021
- [c18]Jidapa Thadajarassiri, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner:
Semi-Supervised Knowledge Amalgamation for Sequence Classification. AAAI 2021: 9859-9867 - [c17]Dongyu Zhang, Cansu Sen, Jidapa Thadajarassiri, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner:
Human-like Explanation for Text Classification With Limited Attention Supervision. IEEE BigData 2021: 957-967 - [c16]Luke Buquicchio, Walter Gerych, Abdulaziz Alajaji, Kavin Chandrasekaran, Hamid Mansoor, Thomas Hartvigsen, Elke A. Rundensteiner, Emmanuel Agu:
Variational Open Set Recognition (VOSR). IEEE BigData 2021: 994-1001 - [c15]Prathyush S. Parvatharaju, Ramesh Doddaiah, Thomas Hartvigsen, Elke A. Rundensteiner:
Learning Saliency Maps to Explain Deep Time Series Classifiers. CIKM 2021: 1406-1415 - [c14]Hang Yin, John Boaz Lee, Xiangnan Kong, Thomas Hartvigsen, Sihong Xie:
Energy-Efficient Models for High-Dimensional Spike Train Classification using Sparse Spiking Neural Networks. KDD 2021: 2017-2025 - [c13]Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Emmanuel Agu, Elke A. Rundensteiner:
Recurrent Bayesian Classifier Chains for Exact Multi-Label Classification. NeurIPS 2021: 15981-15992 - 2020
- [c12]Cansu Sen, Thomas Hartvigsen, Biao Yin, Xiangnan Kong, Elke A. Rundensteiner:
Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words? ACL 2020: 4596-4608 - [c11]Jidapa Thadajarassiri, Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner:
Learning Similarity-Preserving Meta-Embedding for Text Mining. IEEE BigData 2020: 808-817 - [c10]Erin Teeple, Thomas Hartvigsen, Cansu Sen, Kajal T. Claypool, Elke A. Rundensteiner:
Clinical Performance Evaluation of a Machine Learning System for Predicting Hospital-Acquired Clostridium Difficile Infection. HEALTHINF 2020: 656-663 - [c9]Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke A. Rundensteiner:
Learning to Selectively Update State Neurons in Recurrent Networks. CIKM 2020: 485-494 - [c8]Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke A. Rundensteiner:
Recurrent Halting Chain for Early Multi-label Classification. KDD 2020: 1382-1392
2010 – 2019
- 2019
- [c7]Jidapa Thadajarassiri, Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner:
Comparing General and Locally-Learned Word Embeddings for Clinical Text Mining. BHI 2019: 1-4 - [c6]Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner:
Patient-level Classification on Clinical Note Sequences Guided by Attributed Hierarchical Attention. IEEE BigData 2019: 930-939 - [c5]Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner:
Learning Temporal Relevance in Longitudinal Medical Notes. IEEE BigData 2019: 2474-2483 - [c4]Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke A. Rundensteiner:
Adaptive-Halting Policy Network for Early Classification. KDD 2019: 101-110 - 2018
- [c3]Thomas Hartvigsen, Cansu Sen, Sarah Brownell, Erin Teeple, Xiangnan Kong, Elke A. Rundensteiner:
Early Prediction of MRSA Infections using Electronic Health Records. HEALTHINF 2018: 156-167 - [c2]Thomas Hartvigsen, Cansu Sen, Elke A. Rundensteiner:
Detecting MRSA Infections by Fusing Structured and Unstructured Electronic Health Record Data. BIOSTEC (Selected Papers) 2018: 399-419 - 2017
- [c1]Cansu Sen, Thomas Hartvigsen, Elke A. Rundensteiner, Kajal T. Claypool:
CREST - Risk Prediction for Clostridium Difficile Infection Using Multimodal Data Mining. ECML/PKDD (3) 2017: 52-63
Coauthor Index
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last updated on 2024-05-27 23:34 CEST by the dblp team
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