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06/15/2023
FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods
This paper introduces the Fair Fairness Benchmark (), a benchmarking fra...
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03/30/2023
Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant
Organ transplant is the essential treatment method for some end-stage di...
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03/24/2023
Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint
Clinical trials are indispensable in developing new treatments, but they...
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03/19/2023
PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data
Detailed phenotype information is fundamental to accurate diagnosis and ...
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03/02/2023
Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions
Various attribution methods have been developed to explain deep neural n...
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02/18/2023
Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning
Liver transplant is an essential therapy performed for severe liver dise...
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01/03/2023
RELIANT: Fair Knowledge Distillation for Graph Neural Networks
Graph Neural Networks (GNNs) have shown satisfying performance on variou...
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08/25/2022
Shortcut Learning of Large Language Models in Natural Language Understanding: A Survey
Large language models (LLMs) have achieved state-of-the-art performance ...
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07/20/2022
Mitigating Algorithmic Bias with Limited Annotations
Existing work on fairness modeling commonly assumes that sensitive attri...
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06/29/2022
Fair Machine Learning in Healthcare: A Review
Benefiting from the digitization of healthcare data and the development ...
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11/08/2021
Defense Against Explanation Manipulation
Explainable machine learning attracts increasing attention as it improve...
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11/04/2021
Modeling Techniques for Machine Learning Fairness: A Survey
Machine learning models are becoming pervasive in high-stakes applicatio...
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05/28/2021
A General Taylor Framework for Unifying and Revisiting Attribution Methods
Attribution methods provide an insight into the decision-making process ...
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04/14/2021
Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution
Back propagation based visualizations have been proposed to interpret de...
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08/21/2020
A Unified Taylor Framework for Revisiting Attribution Methods
Attribution methods have been developed to understand the decision makin...
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03/12/2020
PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning
Outlier detection is an important task for various data mining applicati...
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12/17/2019
Multi-Channel Graph Convolutional Networks
Graph neural networks (GNN) has been demonstrated to be effective in cla...
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10/07/2019
PyODDS: An End-to-End Outlier Detection System
PyODDS is an end-to end Python system for outlier detection with databas...
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08/23/2019
Fairness in Deep Learning: A Computational Perspective
Deep learning is increasingly being used in high-stake decision making a...
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08/11/2019
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks
Anomaly detection aims to distinguish observations that are rare and dif...
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12/10/2018
