215 citations · 1.3k across the 64 of their papers we have counts for
13 papers · 1 filter
Auditing Algorithmic Fairness in Machine Learning for Health with Severity-Based LOGAN
Anaelia Ovalle, Sunipa Dev, Jieyu Zhao +2
Auditing machine learning-based (ML) healthcare tools for bias is critical to preventing patient harm, especially in communities that disproportionately face health inequities. Gen…
Measuring Fairness of Text Classifiers via Prediction Sensitivity
Satyapriya Krishna, Rahul Gupta, Apurv Verma +3
With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions. Although various fairness definitions have b…
Neuro-Symbolic Entropy Regularization
Kareem Ahmed, Eric Wang, Kai-Wei Chang +1
In structured prediction, the goal is to jointly predict many output variables that together encode a structured object -- a path in a graph, an entity-relation triple, or an order…
Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction
Kareem Ahmed, Eric Wang, Guy Van den Broeck +1
We study the problem of entity-relation extraction in the presence of symbolic domain knowledge. Such knowledge takes the form of an ontology defining relations and their permissib…
On the Transferability of Adversarial Attacksagainst Neural Text Classifier
Liping Yuan, Xiaoqing Zheng, Yi Zhou +2
Deep neural networks are vulnerable to adversarial attacks, where a small perturbation to an input alters the model prediction. In many cases, malicious inputs intentionally crafte…
GPT-GNN: Generative Pre-Training of Graph Neural Networks
Ziniu Hu, Yuxiao Dong, Kuansan Wang +2
Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data. However, training GNNs usually requires abundant task-specific labeled data, w…