18 citations · 43 across the 8 of their papers we have counts for
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Optimization for Classical Machine Learning Problems on the GPU
Sören Laue, Mark Blacher, Joachim Giesen
Constrained optimization problems arise frequently in classical machine learning. There exist frameworks addressing constrained optimization, for instance, CVXPY and GENO. However,…
A Simple and Efficient Tensor Calculus for Machine Learning
Sören Laue, Matthias Mitterreiter, Joachim Giesen
Computing derivatives of tensor expressions, also known as tensor calculus, is a fundamental task in machine learning. A key concern is the efficiency of evaluating the expressions…
GENO -- GENeric Optimization for Classical Machine Learning
Sören Laue, Matthias Mitterreiter, Joachim Giesen
Although optimization is the longstanding algorithmic backbone of machine learning, new models still require the time-consuming implementation of new solvers. As a result, there ar…
Ising Models with Latent Conditional Gaussian Variables
Frank Nussbaum, Joachim Giesen
Ising models describe the joint probability distribution of a vector of binary feature variables. Typically, not all the variables interact with each other and one is interested in…
A Combinatorial Algorithm to Compute Regularization Paths
Bernd Gärtner, Joachim Giesen, Martin Jaggi +1
For a wide variety of regularization methods, algorithms computing the entire solution path have been developed recently. Solution path algorithms do not only compute the solution…