Tom Wollschläger

Computer Science · Physical Sciences

3h-index25citations22works0.62yr avg
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Research Topics

Adversarial Robustness in Machine Learning(4), Topic Modeling(3), Neural Networks and Applications(3), Machine Learning and Algorithms(3), Quantum Information and Cryptography(2)

Publications22 total

The Illusion of Certainty: Uncertainty Quantification for LLMs Fails under Ambiguity
ArXiv.org·2025Open Access
Diffusion LLMs are Natural Adversaries for any LLM
ArXiv.org·2025Open Access
What Expressivity Theory Misses: Message Passing Complexity for GNNs
ArXiv.org·2025Open Access
Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory
ArXiv.org·2025Open Access
REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective
arXiv (Cornell University)·2025Open Access
The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence
arXiv (Cornell University)·2025Open Access
Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives
arXiv (Cornell University)·2025Open Access
Discrete Randomized Smoothing Meets Quantum Computing
2024· 1 cited
Certifiably Robust Encoding Schemes
2024· 1 cited
Certifiably Robust Encoding Schemes
arXiv (Cornell University)·2024Open Access
Discrete Randomized Smoothing Meets Quantum Computing
arXiv (Cornell University)·2024Open Access
Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space
arXiv (Cornell University)·2024· 1 citedOpen Access
Expressivity and Generalization: Fragment-Biases for Molecular GNNs
arXiv (Cornell University)·2024· 3 citedOpen Access
Energy-based Epistemic Uncertainty for Graph Neural Networks
arXiv (Cornell University)·2024Open Access
Uncertainty for Active Learning on Graphs
arXiv (Cornell University)·2024Open Access
Attacking Large Language Models with Projected Gradient Descent
arXiv (Cornell University)·2024· 5 citedOpen Access
Energy-based Epistemic Uncertainty for Graph Neural Networks
2024
Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness
2023· 8 cited
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness
arXiv (Cornell University)·2023· 1 citedOpen Access
Uncertainty Estimation for Molecules: Desiderata and Methods
arXiv (Cornell University)·2023· 5 citedOpen Access
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Frequent Co-authors

Stephan Günnemann(22), Nicola Franco(6), J. Lorenz(6), Dominik Fuchsgruber(5), Aman Saxena(4), Johannes Gasteiger(3), Benedikt Poggel(2), Simon Geisler(2), Mohamed Abdalla(2), Nicholas Gao(2), Bertrand Charpentier(2), Mohamed Amine Ketata(2), Niklas Kemper(2), Johanna Sommer(2), Yan Scholten(2), Leo Schwinn(2), Vincent Cohen-Addad(2), Leon Hetzel(1), Francesco Campi(1), Lukas Gosch(1)