Chaire d'optimisation discrete

Chaire

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Personnes associées (30)

Personnes associées

Chargement

Unités effectuant des recherches similaires (105)

Unités effectuant des recherches similaires

Chargement

Domaines de recheche associés (47)

Domaines de recheche associés

Chargement

Publications associées (88)

Publications associées

Chargement

Metric learning for kernel ridge regression: assessment of molecular similarity

Friedrich Eisenbrand, Raimon Fabregat I De Aguilar-Amat, Puck Elisabeth van Gerwen

Supervised and unsupervised kernel-based algorithms widely used in the physical sciences depend upon the notion of similarity. Their reliance on pre-defined distance metrics-e.g. the Euclidean or Manhattan distance-are problematic especially when used in combination with high-dimensional feature vectors for which the similarity measure does not well-reflect the differences in the target property. Metric learning is an elegant approach to surmount this shortcoming and find a property-informed transformation of the feature space. We propose a new algorithm for metric learning specifically adapted for kernel ridge regression (KRR): metric learning for kernel ridge regression (MLKRR). It is based on the Metric Learning for Kernel Regression framework using the Nadaraya-Watson estimator, which we show to be inferior to the KRR estimator for typical physics-based machine learning tasks. The MLKRR algorithm allows for superior predictive performance on the benchmark regression task of atomisation energies of QM9 molecules, as well as generating more meaningful low-dimensional projections of the modified feature space.

IOP Publishing Ltd2022

Chargement

The double exponential runtime is tight for 2-stage stochastic ILPs

Klaus Jansen, Kim-Manuel Klein, Alexandra Anna Lassota

We consider fundamental algorithmic number theoretic problems and their relation to a class of block structured Integer Linear Programs (ILPs) called 2-stage stochastic. A 2-stage stochastic ILP is an integer program of the form min{c(T)x vertical bar Ax = b, l

SPRINGER HEIDELBERG2022

Chargement

We show that a constant factor approximation of the shortest and closest lattice vector problem in any l(p)-norm can be computed in time 2((0.802 + epsilon)n). This matches the currently fastest constant factor approximation algorithm for the shortest vector problem in the l(2) norm. To obtain our result, we combine the latter algorithm for l(2) with geometric insights related to coverings. (C) 2021 Published by Elsevier Inc.

ACADEMIC PRESS INC ELSEVIER SCIENCE2022

Chargement