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Dissecting the heterogeneity of "in the wild" stress from multimodal sensor data

NPJ digital medicine, 2023-12, Vol.6 (1), p.237-9, Article 237

2023

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Explaining deep multi-class time series classifiers

Knowledge and information systems, 2024-06, Vol.66 (6), p.3497-3521

2024

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

Machine Learning and Knowledge Discovery in Databases, p.52-63

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Demographic bias in misdiagnosis by computational pathology models

Nature medicine, 2024-04, Vol.30 (4), p.1174-1190

2024

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

Biomedical Engineering Systems and Technologies, p.399-419

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

MIT Case Studies in Social and Ethical Responsibilities of Computing, 2023-02 (Winter 2023)

2023

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Knowledge Amalgamation for Multi-Label Classification via Label Dependency Transfer

Proceedings of the ... AAAI Conference on Artificial Intelligence, 2023, Vol.37 (8), p.9980-9988

2023

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Recurrent Halting Chain for Early Multi-label Classification

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, p.1382-1392

2020

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Adaptive-Halting Policy Network for Early Classification

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, p.101-110

2019

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Stop&Hop: Early Classification of Irregular Time Series

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022, p.696-705

2022

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

Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2022, p.1194-1206

2022

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Class-Specific Explainability for Deep Time Series Classifiers

2022 IEEE International Conference on Data Mining (ICDM), 2022, p.101-110

2022

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Learning Saliency Maps to Explain Deep Time Series Classifiers

Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021, p.1406-1415

2021

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

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022, p.582-591

2022

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Recovering the Propensity Score from Biased Positive Unlabeled Data

Proceedings of the ... AAAI Conference on Artificial Intelligence, 2022, Vol.36 (6), p.6694-6702

2022

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Learning to Selectively Update State Neurons in Recurrent Networks

Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020, p.485-494

2020

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

2019 IEEE International Conference on Big Data (Big Data), 2019, p.930-939

2019

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Human-like Explanation for Text Classification With Limited Attention Supervision

2021 IEEE International Conference on Big Data (Big Data), 2021, p.957-967

2021

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Semi-Supervised Knowledge Amalgamation for Sequence Classification

Proceedings of the ... AAAI Conference on Artificial Intelligence, 2021, Vol.35 (11), p.9859-9867

2021

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Learning Similarity-Preserving Meta-Embedding for Text Mining

2020 IEEE International Conference on Big Data (Big Data), 2020, p.808-817

2020

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

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, p.2017-2025

2021

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Variational Open Set Recognition (VOSR)

2021 IEEE International Conference on Big Data (Big Data), 2021, p.994-1001

2021

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Early Diagnosis Prediction with Recurrent Neural Networks

2019 IEEE MIT Undergraduate Research Technology Conference (URTC), 2019, p.1-4

2019

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Taking Off with AI: Lessons from Aviation for Healthcare

Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, 2023, p.1-14

2023

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Learning Temporal Relevance in Longitudinal Medical Notes

2019 IEEE International Conference on Big Data (Big Data), 2019, p.2474-2483

2019

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Dissecting the heterogeneity of "in the wild" stress from multimodal sensor data

Explaining deep multi-class time series classifiers

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

Demographic bias in misdiagnosis by computational pathology models

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

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

Knowledge Amalgamation for Multi-Label Classification via Label Dependency Transfer

Recurrent Halting Chain for Early Multi-label Classification

Adaptive-Halting Policy Network for Early Classification

Stop&Hop: Early Classification of Irregular Time Series

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

Class-Specific Explainability for Deep Time Series Classifiers

Learning Saliency Maps to Explain Deep Time Series Classifiers

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

Recovering the Propensity Score from Biased Positive Unlabeled Data

Learning to Selectively Update State Neurons in Recurrent Networks

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

Human-like Explanation for Text Classification With Limited Attention Supervision

Semi-Supervised Knowledge Amalgamation for Sequence Classification

Learning Similarity-Preserving Meta-Embedding for Text Mining

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

Variational Open Set Recognition (VOSR)

Early Diagnosis Prediction with Recurrent Neural Networks

Taking Off with AI: Lessons from Aviation for Healthcare

Learning Temporal Relevance in Longitudinal Medical Notes

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