Computing Higher Order Derivatives of Matrix and Tensor Expressions. - Sören Laue; Matthias Mitterreiter; Joachim Giesen (NeurIPS, 2018)

GENO - GENeric Optimization for Classical Machine Learning. - Sören Laue; Matthias Mitterreiter; Joachim Giesen (NeurIPS, 2019)

MatrixCalculus.org - Computing Derivatives of Matrix and Tensor Expressions. - Sören Laue; Matthias Mitterreiter; Joachim Giesen (ECML/PKDD (3), 2019)

GENO - Optimization for Classical Machine Learning Made Fast and Easy. - Sören Laue; Matthias Mitterreiter; Joachim Giesen (AAAI, 2020)

A Simple and Efficient Tensor Calculus. - Sören Laue; Matthias Mitterreiter; Joachim Giesen (AAAI, 2020)

Fast Entity Resolution With Mock Labels and Sorted Integer Sets. - Mark Blacher; Joachim Giesen; Sören Laue; Julien Klaus; Matthias Mitterreiter (DI2KG@VLDB, 2020)

A Simple and Efficient Tensor Calculus for Machine Learning. - Sören Laue; Matthias Mitterreiter; Joachim Giesen (Fundam. Informaticae, 2020)

Optimization frameworks for machine learning: Examples and case study. - Joachim Giesen; Sören Laue; Matthias Mitterreiter (it Inf. Technol., 2020)

Method of Moments for Topic Models with Mixed Discrete and Continuous Features. - Joachim Giesen; Paul Kahlmeyer; Sören Laue; Matthias Mitterreiter; Frank Nussbaum; Christoph Staudt; Sina Zarrieß (IJCAI, 2021)

Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption. - Matthias Mitterreiter; Marcel Koch; Joachim Giesen; Sören Laue (AAAI, 2023)

GRay: Ray Casting for Visualization and Interactive Data Exploration of Gaussian Mixture Models. - Kai Lawonn; Monique Meuschke; Pepe Eulzer; Matthias Mitterreiter; Joachim Giesen; Tobias Günther (IEEE Trans. Vis. Comput. Graph., 2023)

Mixed membership Gaussians. - Joachim Giesen; Paul Kahlmeyer; Sören Laue; Matthias Mitterreiter; Frank Nussbaum; Christoph Staudt (J. Multivar. Anal., 2023)