Search Results for author:
Found 8 papers, 1 papers with code
Counter-example guided inductive synthesis of control Lyapunov functions for uncertain systems
We propose a counter-example guided inductive synthesis (CEGIS) scheme for the design of control Lyapunov functions and associated state-feedback controllers for linear systems affected by parametric uncertainty with arbitrary shape.
Hierarchical Clustering and Matrix Completion for the Reconstruction of World Input-Output Tables
World Input-Output (I/O) matrices provide the networks of within- and cross-country economic relations.
Principal Component Analysis Applied to Gradient Fields in Band Gap Optimization Problems for Metamaterials
In this framework, the present article describes the application of a related unsupervised machine learning technique, namely, principal component analysis, to approximate the gradient of the objective function of a band gap optimization problem for an acoustic metamaterial, with the aim of making the successive application of a gradient-based iterative optimization algorithm faster.
On principal component analysis of the convex combination of two data matrices and its application to acoustic metamaterial filters
In this short paper, a matrix perturbation bound on the eigenvalues found by principal component analysis is investigated, for the case in which the data matrix on which principal component analysis is performed is a convex combination of two data matrices.
Machine-learning techniques for the optimal design of acoustic metamaterials
Specifically, the feasibility and effectiveness of Radial Basis Function networks and Quasi-Monte Carlo methods for the interpolation of the objective functions of such optimization problems are discussed, and their numerical application to a specific acoustic metamaterial with tetrachiral microstructure is presented.
Heterogeneous causal effects with imperfect compliance: a Bayesian machine learning approach
This paper introduces an innovative Bayesian machine learning algorithm to draw interpretable inference on heterogeneous causal effects in the presence of imperfect compliance (e. g., under an irregular assignment mechanism).
Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms
This paper provides a link between causal inference and machine learning techniques - specifically, Classification and Regression Trees (CART) - in observational studies where the receipt of the treatment is not randomized, but the assignment to the treatment can be assumed to be randomized (irregular assignment mechanism).
Symmetric and antisymmetric properties of solutions to kernel-based machine learning problems
A particularly interesting instance of supervised learning with kernels is when each training example is associated with two objects, as in pairwise classification (Brunner et al., 2012), and in supervised learning of preference relations (Herbrich et al., 1998).
