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Joachim Giesen
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Publications
- 2023
- [j33]Joachim Giesen, Paul Kahlmeyer, Sören Laue, Matthias Mitterreiter, Frank Nussbaum, Christoph Staudt:
Mixed membership Gaussians. J. Multivar. Anal. 195: 105141 (2023) - [j32]Mark Blacher, Julien Klaus, Christoph Staudt, Sören Laue, Viktor Leis, Joachim Giesen:
Efficient and Portable Einstein Summation in SQL. Proc. ACM Manag. Data 1(2): 121:1-121:19 (2023) - [c79]Matthias Mitterreiter, Marcel Koch, Joachim Giesen, Sören Laue:
Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption. AAAI 2023: 9209-9216 - [i13]Matthias Mitterreiter, Marcel Koch, Joachim Giesen, Sören Laue:
Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption. CoRR abs/2301.01583 (2023) - 2022
- [c77]Sören Laue, Mark Blacher, Joachim Giesen:
Optimization for Classical Machine Learning Problems on the GPU. AAAI 2022: 7300-7308 - [c76]Mark Blacher, Joachim Giesen, Sören Laue, Julien Klaus, Viktor Leis:
Machine Learning, Linear Algebra, and More: Is SQL All You Need? CIDR 2022 - [c73]Julien Klaus, Niklas Merk, Konstantin Wiedom, Sören Laue, Joachim Giesen:
Convexity Certificates from Hessians. NeurIPS 2022 - [p2]Joachim Giesen, Lars Kuehne, Sören Laue:
The GENO Software Stack. Algorithms for Big Data 2022: 213-228 - [i12]Sören Laue, Mark Blacher, Joachim Giesen:
Optimization for Classical Machine Learning Problems on the GPU. CoRR abs/2203.16340 (2022) - [i10]Julien Klaus, Niklas Merk, Konstantin Wiedom, Sören Laue, Joachim Giesen:
Convexity Certificates from Hessians. CoRR abs/2210.10430 (2022) - 2021
- [c72]Joachim Giesen, Paul Kahlmeyer, Sören Laue, Matthias Mitterreiter, Frank Nussbaum, Christoph Staudt, Sina Zarrieß:
Method of Moments for Topic Models with Mixed Discrete and Continuous Features. IJCAI 2021: 2418-2424 - 2020
- [j26]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
A Simple and Efficient Tensor Calculus for Machine Learning. Fundam. Informaticae 177(2): 157-179 (2020) - [j25]Joachim Giesen, Sören Laue, Matthias Mitterreiter:
Optimization frameworks for machine learning: Examples and case study. it Inf. Technol. 62(3-4): 169-180 (2020) - [c69]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
A Simple and Efficient Tensor Calculus. AAAI 2020: 4527-4534 - [c68]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
GENO - Optimization for Classical Machine Learning Made Fast and Easy. AAAI 2020: 13620-13621 - [c66]Mark Blacher, Joachim Giesen, Sören Laue, Julien Klaus, Matthias Mitterreiter:
Fast Entity Resolution With Mock Labels and Sorted Integer Sets. DI2KG@VLDB 2020 - [i9]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
A Simple and Efficient Tensor Calculus for Machine Learning. CoRR abs/2010.03313 (2020) - 2019
- [j23]Joachim Giesen, Julien Klaus, Sören Laue, Ferdinand Schreck:
Visualization Support for Developing a Matrix Calculus Algorithm: A Case Study. Comput. Graph. Forum 38(3): 351-361 (2019) - [c65]Joachim Giesen, Sören Laue, Andreas Löhne, Christopher Schneider:
Using Benson's Algorithm for Regularization Parameter Tracking. AAAI 2019: 3689-3696 - [c62]Joachim Giesen, Sören Laue:
Combining ADMM and the Augmented Lagrangian Method for Efficiently Handling Many Constraints. IJCAI 2019: 4525-4531 - [c61]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
GENO - GENeric Optimization for Classical Machine Learning. NeurIPS 2019: 2187-2198 - [c60]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
MatrixCalculus.org - Computing Derivatives of Matrix and Tensor Expressions. ECML/PKDD (3) 2019: 769-772 - [i7]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
GENO - GENeric Optimization for Classical Machine Learning. CoRR abs/1905.13587 (2019) - 2018
- [c59]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
Computing Higher Order Derivatives of Matrix and Tensor Expressions. NeurIPS 2018: 2755-2764 - 2016
- [i6]Joachim Giesen, Sören Laue:
Distributed Convex Optimization with Many Convex Constraints. CoRR abs/1610.02967 (2016) - 2015
- [c58]Katharina Blechschmidt, Joachim Giesen, Sören Laue:
Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains. ICML 2015: 438-447 - [c57]Joachim Giesen, Sören Laue, Jens K. Müller:
Reconstructing a Sparse Solution from a Compressed Support Vector Machine. MACIS 2015: 305-319 - 2014
- [c56]Joachim Giesen, Sören Laue, Lars Kuehne:
Sketching the Support of a Probability Measure. AISTATS 2014: 257-265 - [c55]Joachim Giesen, Sören Laue, Patrick Wieschollek:
Robust and Efficient Kernel Hyperparameter Paths with Guarantees. ICML 2014: 1296-1304 - 2012
- [j20]Joachim Giesen, Martin Jaggi, Sören Laue:
Approximating parameterized convex optimization problems. ACM Trans. Algorithms 9(1): 10:1-10:17 (2012) - [c53]Joachim Giesen, Martin Jaggi, Sören Laue:
Optimizing over the Growing Spectrahedron. ESA 2012: 503-514 - [c52]Joachim Giesen, Jens K. Müller, Sören Laue, Sascha Swiercy:
Approximating Concavely Parameterized Optimization Problems. NIPS 2012: 2114-2122 - [c51]Joachim Giesen, Martin Jaggi, Sören Laue:
Regularization Paths with Guarantees for Convex Semidefinite Optimization. AISTATS 2012: 432-439 - 2010
- [c48]Joachim Giesen, Martin Jaggi, Sören Laue:
Approximating Parameterized Convex Optimization Problems. ESA (1) 2010: 524-535
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