Zobrazeno 1 - 10
of 2 405
pro vyhledávání: '"G.1.6"'
Autor:
Pav, Steven E.
We generalize the non-negative matrix factorization algorithm of Lee and Seung to accept a weighted norm, and to support ridge and Lasso regularization. We recast the Lee and Seung multiplicative update as an additive update which does not get stuck
Externí odkaz:
http://arxiv.org/abs/2410.22698
We present skwdro, a Python library for training robust machine learning models. The library is based on distributionally robust optimization using optimal transport distances. For ease of use, it features both scikit-learn compatible estimators for
Externí odkaz:
http://arxiv.org/abs/2410.21231
Given a Hilbert space and a finite family of operators defined on the space, the common fixed point problem (CFPP) is the problem of finding a point in the intersection of the fixed point sets of these operators. A particular case of the problem, whe
Externí odkaz:
http://arxiv.org/abs/2410.20448
Autor:
He, Angel Y., Holmes, Mark
We present a computer assisted proof for a result concerning a three player betting game, introduced by Angel and Holmes. The three players start with initial capital $x, y, z > 0$ respectively. At each step of this game two players are selected at r
Externí odkaz:
http://arxiv.org/abs/2410.15329
Autor:
Joshy, Anugrah Jo, Hwang, John T.
Recent advances in computing hardware and modeling software have given rise to new applications for numerical optimization. These new applications occasionally uncover bottlenecks in existing optimization algorithms and necessitate further specializa
Externí odkaz:
http://arxiv.org/abs/2410.12942
Deep learning methods - consisting of a class of deep neural networks (DNNs) trained by a stochastic gradient descent (SGD) optimization method - are nowadays key tools to solve data driven supervised learning problems. Despite the great success of S
Externí odkaz:
http://arxiv.org/abs/2410.10533
We propose new algorithms to efficiently average a collection of points on a Grassmannian manifold in both the centralized and decentralized settings. Grassmannian points are used ubiquitously in machine learning, computer vision, and signal processi
Externí odkaz:
http://arxiv.org/abs/2410.08956
We explore a robust version of the barycenter problem among $n$ centered Gaussian probability measures, termed Semi-Unbalanced Optimal Transport (SUOT)-based Barycenter, wherein the barycenter remains fixed while the others are relaxed using Kullback
Externí odkaz:
http://arxiv.org/abs/2410.08117
Autor:
Jung, Minchan, Kim, Kwangki
This paper introduces the Bidirectional Clustered MPPI (BiC-MPPI) algorithm, a novel trajectory optimization method aimed at enhancing goal-directed guidance within the Model Predictive Path Integral (MPPI) framework. BiC-MPPI incorporates bidirectio
Externí odkaz:
http://arxiv.org/abs/2410.06493
Autor:
Ali, Alejandro Mata, Mencia, Edgar
In this paper, we present a QUBO formulation designed to solve a series of generalisations of the LinkedIn queens game, a version of the N-queens problem. We adapt this formulation for several particular cases of the problem by trying to optimise the
Externí odkaz:
http://arxiv.org/abs/2410.06429