375 citations · 537 across the 4 of their papers we have counts for
4 papers
Fast Proxies for LLM Robustness Evaluation
Tim Beyer, Jan Schuchardt, Leo Schwinn +1
Evaluating the robustness of LLMs to adversarial attacks is crucial for safe deployment, yet current red-teaming methods are often prohibitively expensive. We compare the ability o…
Assessing Robustness via Score-Based Adversarial Image Generation
Marcel Kollovieh, Lukas Gosch, Marten Lienen +3
Most adversarial attacks and defenses focus on perturbations within small -norm constraints. However, threat models cannot capture all relevant semantics-preservin…
Scaling Graph Neural Networks with Approximate PageRank
Aleksandar Bojchevski, Johannes Gasteiger, Bryan Perozzi +5
Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks. However, learning on large graphs remains a challenge - many recently propos…
Adversarial Attacks on Node Embeddings via Graph Poisoning
Aleksandar Bojchevski, Stephan Günnemann
The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks. However, despi…