14 citations · 21 across the 8 of their papers we have counts for
6 papers · 1 filter
Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks
Yuntian He, Pranav Maneriker, Anutam Srinivasan +2
Conformal Prediction is a robust framework that ensures reliable coverage across machine learning tasks. Although recent studies have applied conformal prediction to graph neural n…
Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs
Pranav Maneriker, Aditya T. Vadlamani, Anutam Srinivasan +3
Conformal prediction has become increasingly popular for quantifying the uncertainty associated with machine learning models. Recent work in graph uncertainty quantification has bu…
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs
Yue Zhang, Yuntian He, Saket Gurukar +1
Heterogeneous graphs are ubiquitous in real-world applications because they can represent various relationships between different types of entities. Therefore, learning embeddings…
FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning
Yuntian He, Saket Gurukar, Srinivasan Parthasarathy
Graph representation learning models have demonstrated great capability in many real-world applications. Nevertheless, prior research indicates that these models can learn biased r…
Sepsis Prediction with Temporal Convolutional Networks
Xing Wang, Yuntian He
We design and implement a temporal convolutional network model to predict sepsis onset. Our model is trained on data extracted from MIMIC III database, based on a retrospective ana…
FairEGM: Fair Link Prediction and Recommendation via Emulated Graph Modification
Sean Current, Yuntian He, Saket Gurukar +1
As machine learning becomes more widely adopted across domains, it is critical that researchers and ML engineers think about the inherent biases in the data that may be perpetuated…