5 papers
Quantitative Bounds for Sorting-Based Permutation-Invariant Embeddings
Nadav Dym, Matthias Wellershoff, Efstratios Tsoukanis +2
We study permutation-invariant embeddings of -dimensional point sets, which are defined by sorting independent one-dimensional projections of the input. Such embeddings aris…
An Approximation Theory Perspective on Machine Learning
Hrushikesh N. Mhaskar, Efstratios Tsoukanis, Ameya D. Jagtap
A central problem in machine learning is often formulated as follows: Given a dataset , which is a sample drawn from an unknown probability distribution, th…
G-Invariant Representations using Coorbits: Bi-Lipschitz Properties
Radu Balan, Efstratios Tsoukanis
Consider a finite dimensional real vector space and a finite group acting unitarily on it. We study the general problem of constructing Euclidean stable embeddings of the quotient…
G-Invariant Representations using Coorbits: Injectivity Properties
Radu Balan, Efstratios Tsoukanis
Consider a finite-dimensional real vector space equipped with a finite group acting unitarily on it. We address the general problem of constructing Euclidean stable embeddings of t…
Active Learning Classification from a Signal Separation Perspective
Hrushikesh Mhaskar, Ryan O'Dowd, Efstratios Tsoukanis
In machine learning, classification is usually seen as a function approximation problem, where the goal is to learn a function that maps input features to class labels. In this pap…