Search Results for author:
Found 7 papers, 4 papers with code
PMBO: Enhancing Black-Box Optimization through Multivariate Polynomial Surrogates
We introduce a surrogate-based black-box optimization method, termed Polynomial-model-based optimization (PMBO).
Diffeomorphic Measure Matching with Kernels for Generative Modeling
This article presents a general framework for the transport of probability measures towards minimum divergence generative modeling and sampling using ordinary differential equations (ODEs) and Reproducing Kernel Hilbert Spaces (RKHSs), inspired by ideas from diffeomorphic matching and image registration.
Computational Hypergraph Discovery, a Gaussian Process framework for connecting the dots
Type 1: Approximate an unknown function given input/output data.
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs
We introduce a priori Sobolev-space error estimates for the solution of nonlinear, and possibly parametric, PDEs using Gaussian process and kernel based methods.
Kernel Methods are Competitive for Operator Learning
We present a general kernel-based framework for learning operators between Banach spaces along with a priori error analysis and comprehensive numerical comparisons with popular neural net (NN) approaches such as Deep Operator Net (DeepONet) [Lu et al.] and Fourier Neural Operator (FNO) [Li et al.].
Multiclass classification utilising an estimated algorithmic probability prior
Here we explore how algorithmic information theory, especially algorithmic probability, may aid in a machine learning task.
Learning grounded word meaning representations on similarity graphs
This paper introduces a novel approach to learn visually grounded meaning representations of words as low-dimensional node embeddings on an underlying graph hierarchy.
