Generalization bounds for sparse random feature expansions
From MaRDI portal
Publication:2105118
DOI10.1016/j.acha.2022.08.003zbMath1505.65039arXiv2103.03191OpenAlexW3155603885MaRDI QIDQ2105118
Rachel Ward, Robert Shi, Abolfazl Hashemi, Ufuk Topcu, Hayden Schaeffer, Giang Tran
Publication date: 8 December 2022
Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2103.03191
Computational learning theory (68Q32) Applications of functional analysis in optimization, convex analysis, mathematical programming, economics (46N10) Algorithms for approximation of functions (65D15)
Related Items (2)
HARFE: hard-ridge random feature expansion ⋮ SPADE4: sparsity and delay embedding based forecasting of epidemics
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- A mathematical introduction to compressive sensing
- Learning with infinitely many features
- Sparse Legendre expansions via \(\ell_1\)-minimization
- Learning functions of few arbitrary linear parameters in high dimensions
- A non-adapted sparse approximation of PDEs with stochastic inputs
- Approximation of functions of few variables in high dimensions
- Interpolation via weighted \(\ell_{1}\) minimization
- A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training
- Infinite-dimensional compressed sensing and function interpolation
- On the computational power of circuits of spiking neurons
- A near-stationary subspace for ridge approximation
- Exploiting active subspaces to quantify uncertainty in the numerical simulation of the hyshot II scramjet
- Bayesian Lasso for Semiparametric Structural Equation Models
- The Perceptron: A Model for Brain Functioning. I
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
- Probing the Pareto Frontier for Basis Pursuit Solutions
- On decompositions of multivariate functions
- Universal approximation bounds for superpositions of a sigmoidal function
- Extracting Sparse High-Dimensional Dynamics from Limited Data
- Polynomial approximation via compressed sensing of high-dimensional functions on lower sets
- Sparse Additive Models
- On Recovery of Sparse Signals Via $\ell _{1}$ Minimization
- Extracting Structured Dynamical Systems Using Sparse Optimization With Very Few Samples
- ℓ1 Regularization in Infinite Dimensional Feature Spaces
- Stable signal recovery from incomplete and inaccurate measurements
- Compressed sensing
This page was built for publication: Generalization bounds for sparse random feature expansions