Zobrazeno 1 - 10
of 539
pro vyhledávání: '"Kronqvist, P"'
Autor:
Zhao, Shudian, Kronqvist, Jan
In this paper, we present a novel nonlinear programming-based approach to fine-tune pre-trained neural networks to improve robustness against adversarial attacks while maintaining high accuracy on clean data. Our method introduces adversary-correctio
Externí odkaz:
http://arxiv.org/abs/2409.13770
This paper introduces the Bi-linear consensus Alternating Direction Method of Multipliers (Bi-cADMM), aimed at solving large-scale regularized Sparse Machine Learning (SML) problems defined over a network of computational nodes. Mathematically, these
Externí odkaz:
http://arxiv.org/abs/2405.16267
Clustering is one of the most fundamental tools in data science and machine learning, and k-means clustering is one of the most common such methods. There is a variety of approximate algorithms for the k-means problem, but computing the globally opti
Externí odkaz:
http://arxiv.org/abs/2402.13595
When faced with a limited budget of function evaluations, state-of-the-art black-box optimization (BBO) solvers struggle to obtain globally, or sometimes even locally, optimal solutions. In such cases, one may pursue solution polishing, i.e., a compu
Externí odkaz:
http://arxiv.org/abs/2402.12283
Autor:
Malin Kronqvist Håård
Publikováno v:
Education Inquiry, Pp 1-20 (2024)
There is a global movement of education reform, informed by a neoliberal agenda, emphasising continuous improvement and accountability through performance measurements, surveillance and monitoring. This puts local school actors under constant gaze an
Externí odkaz:
https://doaj.org/article/fe701e35c2db4de69c5f229c19af9a10
This paper describes a simple, but effective sampling method for optimizing and learning a discrete approximation (or surrogate) of a multi-dimensional function along a one-dimensional line segment of interest. The method does not rely on derivative
Externí odkaz:
http://arxiv.org/abs/2307.10463
Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces
This paper presents a framework for computing the Gromov-Wasserstein problem between two sets of points in low dimensional spaces, where the discrepancy is the squared Euclidean norm. The Gromov-Wasserstein problem is a generalization of the optimal
Externí odkaz:
http://arxiv.org/abs/2307.09057
The presented work addresses two-stage stochastic programs (2SPs), a broadly applicable model to capture optimization problems subject to uncertain parameters with adjustable decision variables. In case the adjustable or second-stage variables contai
Externí odkaz:
http://arxiv.org/abs/2305.06785
The number of cancer cases per year is rapidly increasing worldwide. In radiation therapy (RT), radiation from linear accelerators is used to kill malignant tumor cells. Scheduling patients for RT is difficult both due to the numerous medical and tec
Externí odkaz:
http://arxiv.org/abs/2303.10985
In the present article we propose a mixed-integer approximation of adjustable-robust optimization (ARO) problems, that have both, continuous and discrete variables on the lowest level. As these trilevel problems are notoriously hard to solve, we rest
Externí odkaz:
http://arxiv.org/abs/2302.13962