Giesen, Joachim

30 publications

AAAI 2025 Dimension Reduction for Symbolic Regression Paul Kahlmeyer, Markus Fischer, Joachim Giesen

AAAI 2025 Discovering Symmetries of ODEs by Symbolic Regression Paul Kahlmeyer, Niklas Merk, Joachim Giesen

NeurIPS 2025 Exploiting Dynamic Sparsity in Einsum Christoph Staudt, Mark Blacher, Tim Hoffmann, Kaspar Kasche, Olaf Beyersdorff, Joachim Giesen

IJCAI 2024 Convexity Certificates for Symbolic Tensor Expressions Paul Gerhardt Rump, Niklas Merk, Julien Klaus, Maurice Wenig, Joachim Giesen

NeurIPS 2024 Einsum Benchmark: Enabling the Development of Next-Generation Tensor Execution Engines Mark Blacher, Christoph Staudt, Julien Klaus, Maurice Wenig, Niklas Merk, Alexander Breuer, Max Engel, Sören Laue, Joachim Giesen

AAAI 2024 Model Counting and Sampling via Semiring Extensions Andreas Goral, Joachim Giesen, Mark Blacher, Christoph Staudt, Julien Klaus

IJCAI 2024 Scaling up Unbiased Search-Based Symbolic Regression Paul Kahlmeyer, Joachim Giesen, Michael Habeck, Henrik Voigt

PGM 2024 Serving MPE Queries on Tensor Networks by Computing Derivatives Maurice Wenig, Hanno Barschel, Joachim Giesen, Andreas Goral, Mark Blacher

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

NeurIPS 2022 Convexity Certificates from Hessians Julien Klaus, Niklas Merk, Konstantin Wiedom, Sören Laue, Joachim Giesen

IJCAI 2022 Leveraging the Wikipedia Graph for Evaluating Word Embeddings Joachim Giesen, Paul Kahlmeyer, Frank Nussbaum, Sina Zarrieß

AAAI 2022 Optimization for Classical Machine Learning Problems on the GPU Sören Laue, Mark Blacher, Joachim Giesen

IJCAI 2021 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

UAI 2021 Robust Principal Component Analysis for Generalized Multi-View Models Frank Nussbaum, Joachim Giesen

AAAI 2020 A Simple and Efficient Tensor Calculus Sören Laue, Matthias Mitterreiter, Joachim Giesen

IJCAI 2020 Disentangling Direct and Indirect Interactions in Polytomous Item Response Theory Models Frank Nussbaum, Joachim Giesen

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

IJCAI 2019 Combining ADMM and the Augmented Lagrangian Method for Efficiently Handling Many Constraints Joachim Giesen, Sören Laue

IJCAI 2019 Efficient Regularization Parameter Selection for Latent Variable Graphical Models via Bi-Level Optimization Joachim Giesen, Frank Nussbaum, Christopher Schneider

NeurIPS 2019 GENO -- GENeric Optimization for Classical Machine Learning Soeren Laue, Matthias Mitterreiter, Joachim Giesen

ALT 2019 Ising Models with Latent Conditional Gaussian Variables Frank Nussbaum, Joachim Giesen

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

AAAI 2019 Using Benson's Algorithm for Regularization Parameter Tracking Joachim Giesen, Sören Laue, Andreas Löhne, Christopher Schneider

NeurIPS 2018 Computing Higher Order Derivatives of Matrix and Tensor Expressions Soeren Laue, Matthias Mitterreiter, Joachim Giesen

ICML 2015 Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains Katharina Blechschmidt, Joachim Giesen, Soeren Laue

ICML 2014 Robust and Efficient Kernel Hyperparameter Paths with Guarantees Joachim Giesen, Soeren Laue, Patrick Wieschollek

AISTATS 2014 Sketching the Support of a Probability Measure Joachim Giesen, Sören Laue, Lars Kuehne

NeurIPS 2012 Approximating Concavely Parameterized Optimization Problems Joachim Giesen, Jens Mueller, Soeren Laue, Sascha Swiercy

AISTATS 2012 Regularization Paths with Guarantees for Convex Semidefinite Optimization Joachim Giesen, Martin Jaggi, Soeren Laue

NeurIPS 2004 Kernel Methods for Implicit Surface Modeling Joachim Giesen, Simon Spalinger, Bernhard Schölkopf