29 citations · 60 across the 10 of their papers we have counts for
13 papers
Gradient is All You Need? How Consensus-Based Optimization can be Interpreted as a Stochastic Relaxation of Gradient Descent
Konstantin Riedl, Timo Klock, Carina Geldhauser +1
In this paper, we provide a novel analytical perspective on the theoretical understanding of gradient-based learning algorithms by interpreting consensus-based optimization (CBO),…
Finite Sample Identification of Wide Shallow Neural Networks with Biases
Massimo Fornasier, Timo Klock, Marco Mondelli +1
Artificial neural networks are functions depending on a finite number of parameters typically encoded as weights and biases. The identification of the parameters of the network fro…
Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoders
Stefan C. Schonsheck, Scott Mahan, Timo Klock +2
Autoencoding is a popular method in representation learning. Conventional autoencoders employ symmetric encoding-decoding procedures and a simple Euclidean latent space to detect h…
Convergence of Anisotropic Consensus-Based Optimization in Mean-Field Law
Massimo Fornasier, Timo Klock, Konstantin Riedl
In this paper we study anisotropic consensus-based optimization (CBO), a multi-agent metaheuristic derivative-free optimization method capable of globally minimizing nonconvex and…
Landscape analysis of an improved power method for tensor decomposition
Joe Kileel, Timo Klock, João M. Pereira
In this work, we consider the optimization formulation for symmetric tensor decomposition recently introduced in the Subspace Power Method (SPM) of Kileel and Pereira. Unlike popul…
Consensus-Based Optimization Methods Converge Globally
Massimo Fornasier, Timo Klock, Konstantin Riedl
In this paper, we study consensus-based optimization (CBO), which is a multi-agent metaheuristic derivative-free optimization method that can globally minimize nonconvex nonsmooth…