Nonsmooth Optimization-Based Hyperparameter-Free Neural Networks for Large-Scale Regression
In this paper, a new nonsmooth optimization-based algorithm for solving large-scale
regression problems is introduced. The regression problem is modeled as fully-connected …
regression problems is introduced. The regression problem is modeled as fully-connected …
Faster algorithms for learning convex functions
The task of approximating an arbitrary convex function arises in several learning problems
such as convex regression, learning with a difference of convex (DC) functions, and learning …
such as convex regression, learning with a difference of convex (DC) functions, and learning …
Predicting pairwise interaction affinities with ℓ0-penalized least squares–a nonsmooth bi-objective optimization based approach
P Paasivirta, R Numminen, A Airola… - Optimization Methods …, 2024 - Taylor & Francis
In this paper, we introduce a novel nonsmooth optimization-based method LMBM-Kron ℓ 0
LS for solving large-scale pairwise interaction affinity prediction problems. The aim of LMBM …
LS for solving large-scale pairwise interaction affinity prediction problems. The aim of LMBM …
Global minimization of a minimum of a finite collection of functions
G Van Dessel, F Glineur - arXiv preprint arXiv:2412.04625, 2024 - arxiv.org
We consider the global minimization of a particular type of minimum structured optimization
problems wherein the variables must belong to some basic set, the feasible domain is …
problems wherein the variables must belong to some basic set, the feasible domain is …
Robust segmented regression: application to oxygen uptake plateau identification
AJQ Sarnaglia, FA Fajardo Molinares… - … and Ecological Statistics, 2023 - Springer
Recently, segmented regression has been utilized as a “working” model for a bootstrap test
to detect true oxygen uptake plateau. This approach employs an iterative procedure based …
to detect true oxygen uptake plateau. This approach employs an iterative procedure based …
Training Single-Layer Morphological Perceptron Using Convex-Concave Programming
I Cunha, ME Valle - 2023 IEEE Latin American Conference on …, 2023 - ieeexplore.ieee.org
This paper concerns the training of a single-layer morphological perceptron using
disciplined convex-concave programming (DCCP). We introduce an algorithm referred to as …
disciplined convex-concave programming (DCCP). We introduce an algorithm referred to as …
Convex regression and its extensions to learning a Bregman divergence and difference of convex functions
A Siahkamari - 2022 - search.proquest.com
Nonparametric convex regression has been extensively studied over the last two decades. It
has been shown any Lipschitz convex function can be approximated with arbitrarily …
has been shown any Lipschitz convex function can be approximated with arbitrarily …
[引用][C] Faster algorithms for learning convex functions
The task of approximating an arbitrary convex function arises in several learning problems
such as convex regression, learning with a difference of convex (DC) functions, and learning …
such as convex regression, learning with a difference of convex (DC) functions, and learning …