6 citations · 12 across the 9 of their papers we have counts for
9 papers
Overcoming Shortcut Learning in Graph Neural Networks through Active Explanation Guidance
Taraneh Younesian, Steve Azzolin, Antonio Longa +3
Graph Neural Networks (GNNs) can solve prediction tasks by unintentionally exploiting shortcuts---that is, edges, nodes, and features that correlate with but are not causal for the…
Hierarchical Conformal Classification
Floris den Hengst, Inès Blin, Majid Mohammadi +2
Conformal prediction (CP) is a powerful framework for quantifying uncertainty in machine learning models, offering reliable predictions with finite-sample coverage guarantees. When…
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Taraneh Younesian, Daniel Daza, Emile van Krieken +2
Graph neural networks (GNNs) learn to represent nodes by aggregating information from their neighbors. As GNNs increase in depth, their receptive field grows exponentially, leading…
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators
Cosmin Octavian Pene, Amirmasoud Ghiassi, Taraneh Younesian +2
Multi-label learning is an emerging extension of the multi-class classification where an image contains multiple labels. Not only acquiring a clean and fully labeled dataset in mul…
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models
Taraneh Younesian, Chi Hong, Amirmasoud Ghiassi +2
Labeling real-world datasets is time consuming but indispensable for supervised machine learning models. A common solution is to distribute the labeling task across a large number…
Active Learning for Noisy Data Streams Using Weak and Strong Labelers
Taraneh Younesian, Dick Epema, Lydia Y. Chen
Labeling data correctly is an expensive and challenging task in machine learning, especially for on-line data streams. Deep learning models especially require a large number of cle…