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Wenjing Lu

2 accepted papers

2026

Calibrating Uncertainty for Zero-Shot Adversarial CLIP

ICML 2026poster

CLIP delivers strong zero-shot classification but remains highly vulnerable to adversarial attacks. Prior adversarial fine-tuning work largely focuses on matching the predicted logits between clean and adversarial examples, which overlooks uncertainty calibration and may degrade the zero-shot genera…

Cited by 0SourceScholar
2024

An Efficient Prototype-Based Clustering Approach for Edge Pruning in Graph Neural Networks to Battle Over-Smoothing

IJCAI 2024poster

Topology augmentation is a popular strategy to address the issue of over-smoothing in graph neural networks (GNNs). To prevent potential distortion of node representations, an essential principle is to enhance the separability between embeddings of nodes from different classes while preserving smoot…