Explanations can be manipulated and geometry is to blame. - Ann-Kathrin Dombrowski; Maximilian Alber; Christopher J. Anders; Marcel Ackermann 001; Klaus-Robert Müller; Pan Kessel (NeurIPS, 2019)

Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling. - Kim Nicoli; Pan Kessel; Nils Strodthoff; Wojciech Samek; Klaus-Robert Müller; Shinichi Nakajima (CoRR, 2019)

Asymptotically Unbiased Generative Neural Sampling. - Kim A. Nicoli; Shinichi Nakajima; Nils Strodthoff; Wojciech Samek; Klaus-Robert Müller; Pan Kessel (CoRR, 2019)

Fairwashing explanations with off-manifold detergent. - Christopher J. Anders; Plamen Pasliev; Ann-Kathrin Dombrowski; Klaus-Robert Müller; Pan Kessel (ICML, 2020)

On Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models. - Kim A. Nicoli; Christopher J. Anders; Lena Funcke; Tobias Hartung; Karl Jansen; Pan Kessel; Shinichi Nakajima; Paolo Stornati (CoRR, 2020)

Deep learning for surrogate modelling of 2D mantle convection. - Siddhant Agarwal; Nicola Tosi; Pan Kessel; Doris Breuer; Grégoire Montavon (CoRR, 2021)

Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse. - Kim A. Nicoli; Christopher J. Anders; Lena Funcke; Tobias Hartung; Karl Jansen; Pan Kessel; Shinichi Nakajima; Paolo Stornati (CoRR, 2021)

Path-Gradient Estimators for Continuous Normalizing Flows. - Lorenz Vaitl; Kim Andrea Nicoli; Shinichi Nakajima; Pan Kessel (ICML, 2022)

Towards robust explanations for deep neural networks. - Ann-Kathrin Dombrowski; Christopher J. Anders; Klaus-Robert Müller; Pan Kessel (Pattern Recognit., 2022)

Gradients should stay on path: better estimators of the reverse- and forward KL divergence for normalizing flows. - Lorenz Vaitl; Kim A. Nicoli; Shinichi Nakajima; Pan Kessel (Mach. Learn. Sci. Technol., 2022)

Physics-Informed Bayesian Optimization of Variational Quantum Circuits. - Kim Nicoli; Christopher J. Anders; Lena Funcke; Tobias Hartung; Karl Jansen; Stefan Kühn; Klaus-Robert Müller; Paolo Stornati; Pan Kessel; Shinichi Nakajima (NeurIPS, 2023)

Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories. - Kim A. Nicoli; Christopher J. Anders; Tobias Hartung; Karl Jansen; Pan Kessel; Shinichi Nakajima (CoRR, 2023)

Batched Predictors Generalize within Distribution. - Andreas Loukas; Pan Kessel (CoRR, 2023)

Fast and unified path gradient estimators for normalizing flows. - Lorenz Vaitl; Ludwig Winkler; Lorenz Richter; Pan Kessel (ICLR, 2024)

Emergent Equivariance in Deep Ensembles. - Jan E. Gerken; Pan Kessel (ICML, 2024)

Equivariant Neural Tangent Kernels. - Philipp Misof; Pan Kessel; Jan E. Gerken (CoRR, 2024)

Diffeomorphic Counterfactuals With Generative Models. - Ann-Kathrin Dombrowski; Jan E. Gerken; Klaus-Robert Müller; Pan Kessel (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins. - Frédéric A. Dreyer; Jan Ludwiczak; Karolis Martinkus; Brennan Abanades; Robert G. Alberstein; Pan Kessel; Pranav Rao; Jae Hyeon Lee; Richard Bonneau; Andrew M. Watkins; Franziska Seeger (CoRR, 2025)

Do we need equivariant models for molecule generation? - Ewa M. Nowara; Joshua Rackers; Patricia Suriana; Pan Kessel; Max Shen 002; Andrew Martin Watkins; Michael Maser (CoRR, 2025)