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cs.LG2025
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…
cs.LG2025
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…
cs.LG2025
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…