Fast cross-validation via sequential testing. - Tammo Krueger; Danny Panknin; Mikio L. Braun (J. Mach. Learn. Res., 2015)

Sharing Hash Codes for Multiple Purposes. - Wikor Pronobis; Danny Panknin; Johannes Kirschnick; Vignesh Srinivasan; Wojciech Samek; Volker Markl; Manohar Kaul; Klaus-Robert Müller; Shinichi Nakajima (CoRR, 2016)

Validity of Time Reversal for Testing Granger Causality. - Irene Winkler; Danny Panknin; Daniel Bartz; Klaus-Robert Müller; Stefan Haufe (IEEE Trans. Signal Process., 2016)

Local Bandwidth Estimation via Mixture of Gaussian Processes. - Danny Panknin; Shinichi Nakajima; Thanh Binh Bui; Klaus-Robert Müller (CoRR, 2019)

Optimal Sampling Density for Nonparametric Regression. - Danny Panknin; Shinichi Nakajima; Klaus-Robert Müller (CoRR, 2021)

Local Function Complexity for Active Learning via Mixture of Gaussian Processes. - Danny Panknin; Stefan Chmiela; Klaus-Robert Müller; Shinichi Nakajima (Trans. Mach. Learn. Res., 2023)

Towards model-agnostic active learning in regression via identification of problem-intrinsic properties. - Danny Panknin (2023)

EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods. - Benedict Clark; Rick Wilming; Artur Dox; Paul Eschenbach; Sami Hached; Daniel Jin Wodke; Michias Taye Zewdie; Uladzislau Bruila; Marta Oliveira; Hjalmar Schulz; Luca Matteo Cornils; Danny Panknin; et al. (CoRR, 2024)

Explainable AI needs formal notions of explanation correctness. - Stefan Haufe; Rick Wilming; Benedict Clark; Rustam Zhumagambetov; Danny Panknin; Ahcène Boubekki (CoRR, 2024)