Deep contract design via discontinuous networks

T Wang, P Dütting, D Ivanov… - Advances in …, 2023 - proceedings.neurips.cc
Contract design involves a principal who establishes contractual agreements about
payments for outcomes that arise from the actions of an agent. In this paper, we initiate the …

Manifold learning by mixture models of VAEs for inverse problems

GS Alberti, J Hertrich, M Santacesaria… - Journal of Machine …, 2024 - jmlr.org
Representing a manifold of very high-dimensional data with generative models has been
shown to be computationally efficient in practice. However, this requires that the data …

Learning with partition of unity-based Kriging estimators

R Cavoretto, A De Rossi, E Perracchione - Applied Mathematics and …, 2023 - Elsevier
For supervised regression tasks we propose and study a new tool, namely Kriging Estimator
based on the Partition of Unity (KEPU) method. Its background belongs to the framework of …

A novel target value standardization method based on cumulative distribution functions for training artificial neural networks

WM Kwok, G Streftaris, SC Dass - 2023 IEEE 13th Symposium …, 2023 - ieeexplore.ieee.org
Function approximation by artificial neural networks (ANNs) are often carried out via a
collocation grid approach. However, for certain combinations of grids and functions, the …

Manifold Learning and Sparsity Priors for Inverse Problems

S Sciutto - 2024 - tesidottorato.depositolegale.it
In this thesis we investigate two distinct regularizing approaches for solving inverse
problems. The first approach involves assuming that the unknown belongs to a manifold …