Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning. In this paper, we first show that the least-squares ...
The problem of learning a sparse conic combination of kernel functions or kernel matrices for classification or regression can be achieved via the regularization by a block 1-norm...
Francis R. Bach, Romain Thibaux, Michael I. Jordan
This paper considers nonlinear modeling based on a limited amount of experimental data and a simulator built from prior knowledge. The problem of how to best incorporate the data ...
: This paper describes a new algorithm for merging the results of remote collections in a distributed information retrieval environment. The algorithm makes use only of the ranks o...
Georgios Paltoglou, Michail Salampasis, Maria Satr...
Pre-symptomatic drought stress prediction is of great relevance in precision plant protection, ultimately helping to meet the challenge of “How to feed a hungry world?”. Unfor...
Kristian Kersting, Zhao Xu, Mirwaes Wahabzada, Chr...