Schwinn, Leo

24 publications

ICLRW 2025 A Generative Approach to LLM Harmfulness Detection with Red Flag Tokens Sophie Xhonneux, David Dobre, Mehrnaz Mofakhami, Leo Schwinn, Gauthier Gidel

ICLR 2025 A Probabilistic Perspective on Unlearning and Alignment for Large Language Models Yan Scholten, Stephan Günnemann, Leo Schwinn

TMLR 2025 A Unified Approach Towards Active Learning and Out-of-Distribution Detection Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann

TMLR 2025 Adversarial Robustness of Graph Transformers Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann

ICML 2025 Efficient Time Series Processing for Transformers and State-Space Models Through Token Merging Leon Götz, Marcel Kollovieh, Stephan Günnemann, Leo Schwinn

NeurIPS 2025 FOCUS: Internal MLLM Representations for Efficient Fine-Grained Visual Question Answering Liangyu Zhong, Fabio Rosenthal, Joachim Sicking, Fabian Hüger, Thorsten Bagdonat, Hanno Gottschalk, Leo Schwinn

ICLRW 2025 Fast Proxies for LLM Robustness Evaluation Tim Beyer, Jan Schuchardt, Leo Schwinn, Stephan Günnemann

ICLR 2025 Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting Marcel Kollovieh, Marten Lienen, David Lüdke, Leo Schwinn, Stephan Günnemann

CVPR 2025 Joint Out-of-Distribution Filtering and Data Discovery Active Learning Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann

NeurIPS 2025 Joint Relational Database Generation via Graph-Conditional Diffusion Models Mohamed Amine Ketata, David Lüdke, Leo Schwinn, Stephan Günnemann

ICML 2025 When to Retrain a Machine Learning Model Florence Regol, Leo Schwinn, Kyle Sprague, Mark Coates, Thomas Markovich

TMLR 2024 Assessing Robustness via Score-Based Adversarial Image Generation Marcel Kollovieh, Lukas Gosch, Marten Lienen, Yan Scholten, Leo Schwinn, Stephan Günnemann

NeurIPS 2024 Efficient Adversarial Training in LLMs with Continuous Attacks Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann, Gauthier Gidel, Leo Schwinn

NeurIPSW 2024 Efficient Time Series Processing for Transformers and State-Space Models Through Token Merging Leon Götz, Marcel Kollovieh, Stephan Günnemann, Leo Schwinn

NeurIPSW 2024 Extracting Unlearned Information from LLMs with Activation Steering Atakan Seyitoğlu, Aleksei Kuvshinov, Leo Schwinn, Stephan Günnemann

NeurIPS 2024 On the Scalability of Certified Adversarial Robustness with Generated Data Thomas Altstidl, David Dobre, Arthur Kosmala, Björn Eskofier, Gauthier Gidel, Leo Schwinn

ICMLW 2024 Relaxing Graph Transformers for Adversarial Attacks Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann

NeurIPS 2024 Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs Through the Embedding Space Leo Schwinn, David Dobre, Sophie Xhonneux, Gauthier Gidel, Stephan Günnemann

NeurIPSW 2023 Adversarial Attacks and Defenses in Large Language Models: Old and New Threats Leo Schwinn, David Dobre, Stephan Günnemann, Gauthier Gidel

AAAI 2023 FastAMI - A Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics Kai Klede, Leo Schwinn, Dario Zanca, Björn M. Eskofier

TMLR 2022 Behind the Machine’s Gaze: Neural Networks with Biologically-Inspired Constraints Exhibit Human-like Visual Attention Leo Schwinn, Doina Precup, Bjoern Eskofier, Dario Zanca

ICML 2022 Improving Robustness Against Real-World and Worst-Case Distribution Shifts Through Decision Region Quantification Leo Schwinn, Leon Bungert, An Nguyen, René Raab, Falk Pulsmeyer, Doina Precup, Bjoern Eskofier, Dario Zanca

NeurIPSW 2022 Simulating Human Gaze with Neural Visual Attention Leo Schwinn, Doina Precup, Bjoern Eskofier, Dario Zanca

UAI 2021 Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Bjoern Eskofier