Multi-Objective Hyperparameter Optimization - An Overview. - Florian Karl; Tobias Pielok; Julia Moosbauer; Florian Pfisterer; Stefan Coors; Martin Binder; Lennart Schneider; Janek Thomas; Jakob Richter; Michel Lang; Eduardo C. Garrido-Merchán; Jürgen Branke; et al. (CoRR, 2022)

Approximate Bayesian Inference with Stein Functional Variational Gradient Descent. - Tobias Pielok; Bernd Bischl; David Rügamer (ICLR, 2023)

Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. - Bernd Bischl; Martin Binder; Michel Lang; Tobias Pielok; Jakob Richter; Stefan Coors; Janek Thomas; Theresa Ullmann; Marc Becker; Anne-Laure Boulesteix; Difan Deng; Marius Lindauer (WIREs Data. Mining. Knowl. Discov., 2023)

Multi-Objective Hyperparameter Optimization in Machine Learning - An Overview. - Florian Karl; Tobias Pielok; Julia Moosbauer; Florian Pfisterer; Stefan Coors; Martin Binder; Lennart Schneider; Janek Thomas; Jakob Richter; Michel Lang; Eduardo C. Garrido-Merchán; Jürgen Branke; et al. (ACM Trans. Evol. Learn. Optim., 2023)

How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression. - Lucas Kook; Chris Kolb; Philipp Schiele; Daniel Dold; Marcel Arpogaus; Cornelius Fritz; Philipp F. M. Baumann; Philipp Kopper; Tobias Pielok; Emilio Dorigatti; David Rügamer (UAI, 2024)

Revisiting Unbiased Implicit Variational Inference. - Tobias Pielok; Bernd Bischl; David Rügamer (CoRR, 2025)

Semi-Implicit Variational Inference via Kernelized Path Gradient Descent. - Tobias Pielok; Bernd Bischl; David Rügamer (CoRR, 2025)