Highlights: NeurIPS (3), ACL (2), KDD (3), AAAI (3), CIKM (4), IEEE BigData (6), FAccT, EAMMO, ICDM, SDM, npj Digital Medicine

2023

Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adapters
Thomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, Marzyeh Ghassemi
NeurIPS, 2023 [pdf][code]

Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency
Owen Queen, Thomas Hartvigsen, Teddy Koker, Huan He, Theodoros Tsiligkaridis, Marinka Zitnik
NeurIPS, 2023 (Spotlight) [pdf]

Dissecting the Heterogeneity of "In-the-Wild Stress" from Multimodal Sensor Data
Sujay Nagaraj, Sarah Goodday, Thomas Hartvigsen, Adrien Boch, Luca Foschini, Marzyeh Ghassemi, Stephen Friend, Anna Goldenberg
npj Digital Medicine.

Knowledge Amalgamation for Multi-Label Classification via Label Dependency Transfer
Jidapa Thadajarassiri, Thomas Hartvigsen, Walter Gerych, Xiangnan Kong, Elke Rundensteiner
AAAI, 2023 [pdf]

Taking Off with AI: Lessons from Aviation for Healthcare

Elizabeth Bondi-Kelly, Thomas Hartvigsen, Lindsay Sanneman, Swami Sankaranarayanan, Lauren Oakden-Rayder, Leo Celi, Julie Shah, Marzyeh Ghassemi.

EAMMO, 2023

Multi-State Brain Network Discovery

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

IEEE BigData, 2023

Stabilizing Adversarial Training for Generative Networks

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

IEEE BigData, 2023

Algorithmic Fairness in Chest X-Ray Diagnosis: A Case Study

Haoran Zhang, Thomas Hartvigsen, Marzyeh Ghassemi
SERC, 2023. [pdf]

A Pipeline for Interpretable Clinical Subtyping with Deep Metric Learning

Haoran Zhang, Qixuan Jin, Thomas Hartvigsen, Miriam Udler, Marzyeh Ghassemi
ICML 2023 Workshop on Interpretable Machine Learning for Healthcare. Best Paper. [pdf]

Continuous Time Evidential Distributions for Irregular Time Series

Taylor Killian, Haoran Zhang, Thomas Hartvigsen, Ava Amini
Working paper at ICML 2023 Workshop on Interpretable Machine Learning for Healthcare. [pdf] [code]

Unraveling the Effects of Age-Based Distribution Shifts on Medical Image Classifiers

Kumail Alhamoud, Yasir Ghunaim, Motasem Alfarra, Philip Torr, Thomas Hartvigsen, Bernard Ghanema, Adel Bibi, Marzyeh Ghassemi.

MusIML Workshop at NeurIPS 2023

Identifying Implicit Social Biases in Vision-Language Models

Kimia Hamidieh, Haoran Zhang, Thomas Hartvigsen, Marzyeh Ghassemi
ICML 2023 Workshop on Data-Centric Machine Learning Research.

Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks
Thomas Hartvigsen, Jidapa Thadajarassiri, Xiangnan Kong, Elke Rundensteiner
[preprint]

Interpretable Unified Language Checking
Tianhua Zhang, Hongyin Luo, Yung-Sung Chuang, Wei Fang, Luc Gaitskell, Thomas Hartvigsen, Xixin Wu, Danny Fox, Helen Meng, James Glass
[preprint]

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
ACL, 2022. pdf // code

Stop&Hop: Early Classification of Irregular Time Series
Thomas Hartvigsen, Walter Gerych, Jidapa Thadajarassiri, Xiangnan Kong, Elke Rundensteiner
CIKM, 2022. pdf // code

Robust Recurrent Classifier Chains For Multi-Label Learning With Missing Labels
Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Emmanuel Agu, Elke Rundensteiner
CIKM, 2022. pdf

Class-Specific Explainability for Deep Time Series Classifiers

Ramesh Doddaiah, Prathyush Parvatharaju, Elke Rundensteiner, Thomas Hartvigsen
ICDM, 2022. pdf // code

Recovering the Propensity Score from Biased Positive Unlabeled Data
Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Emmanuel Agu, Elke Rundensteiner
AAAI, 2022. Oral Spotlight. pdf

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
FAccT, 2022. pdf // code

Positive Unlabeled Learning with a Sequential Selection Bias

Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Kavin Chandrasekaran, Abdulaziz Alajaji, Hamid Mansoor, Elke Rundensteiner, Emmanuel Agu
SDM, 2022. pdf

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

Ruofan Hu, Dongyu Zhang, Dandan Tao, Thomas Hartvigsen, Hao Feng, Elke Rundensteiner
LREC, 2022. pdf

On Detecting COVID-Risky Behavior from Smartphones

Thomas Hartvigsen*, Walter Gerych*, Marzyeh Ghassemi
Workshop on Epidemiology meets Data Mining and Knowledge Discovery, KDD 2022. pdf

Multimodal Checklists for Fair Clinical Decision Support
Qixuan Jin, Haoran Zhang, Thomas Hartvigsen, Marzyeh Ghassemi
Workshop on Learning from Time Series for Health, NeurIPS 2022. Oral Spotlight.

Real-world Relevance of Generative Counterfactual Explanations.
Swami Sankaranarayanan, Thomas Hartvigsen, Lauren Oakden-Rayner, Marzyeh Ghassemi, Philip Isola

Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022.

2021

Recurrent Bayesian Classifier Chains for Exact Multi-label Classification
Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Emmanuel Agu, Elke Rundensteiner
NeurIPS, 2021. pdf

Learning Saliency Maps to Explain Deep Time Series Classifiers

Prathyush Parvatharaju, Ramesh Doddaiah, Thomas Hartvigsen, Elke Rundensteiner

CIKM, 2021. pdf // code

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

KDD, 2021. pdf

Semi-Supervised Knowledge Amalgamation for Sequence Classification

Jidapa Thadajarassiri, Thomas Hartvigsen, Xiangnan Kong, Elke Rundensteiner

AAAI, 2021. pdf // code // talk

Variational Open-Set Recognition
Luke Buquicchio, Walter Gerych, Kavin Chandrasekaran, Abdulaziz Alajaji, Hamid Mansoor, Thomas Hartvigsen, Emmanuel Agu, Elke Rundensteiner
IEEE BigData, 2021.

Explainable Text Classification with Partially-Labeled Human Attention
Dongyu Zhang, Cansu Sen, Jidapa Thadajarassiri, Thomas Hartvigsen, Xiangnan Kong, Elke Rundensteiner
IEEE BigData, 2021.

2020

Recurrent Halting Chain for Early Multi-label Classification

Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke Rundensteiner

KDD, 2020. pdf

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

Cansu Sen, Thomas Hartvigsen, Biao Yin, X. Kong, E. Rundensteiner.

ACL, 2020. pdf


Learning to Selectively Update State Neurons in Recurrent Networks

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

CIKM, 2020. pdf

Learning Similarity-Preserving Meta-Embedding for Text Mining
Jidapa Thadajarassiri, Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke Rundensteiner.
 IEEE BigData, 2020. pdf

Clinical Performance Evaluation of a Machine Learning System for Predicting Hospital-Acquired C. Diff.

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

HEALTHINF, 2020. Best poster. pdf

2019

Adaptive-Halting Policy Network for Early Classification

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

KDD, 2019. pdf

Patient-Level Classification of Clinical Note Sequences Guided by Attributed Hierarchical Attention
Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke Rundensteiner.
 IEEE BigData, 2019.

Learning Temporal Relevance in Longitudinal Medical Notes

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

IEEE BigData, 2019.

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

Jidapa Thadajarassiri, Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke Rundensteiner.
 IEEE BHI, 2019. pdf

Early Diagnosis Prediction with Recurrent Neural Networks
Daniel Johnston, Liubuo Klindziuk, Lolita Nazarov, Thomas Hartvigsen, Elke Rundensteiner.
 IEEE URTC, 2019.

2018

Detecting MRSA Infections by Fusing Structured and Unstructured Electronic Health Record Data
Thomas Hartvigsen, Cansu Sen, Elke Rundensteiner.
 Communications in Computer and Information Science, Volume 2024, 2018.

Early Prediction of MRSA Infections using Electronic Health Records

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

HEALTHINF, 2018. Best student paper runner up. pdf

Handling Missing Values in Multivariate Time Series Classification
Julia Friend, Alec Hauck, Sruthi Kurada, Cansu Sen, Thomas Hartvigsen, E. Rundensteiner.

IEEE URTC, 2018.

2017

CREST - Risk Prediction for Clostridium Difficile Infection Using Multimodal Data Mining
Cansu Sen, Thomas Hartvigsen, Kajal Claypool, Elke Rundensteiner.

ECML, 2017. pdf