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Juan M. Cardenas

2 accepted papers

2024

A Unified Framework for Learning with Nonlinear Model Classes from Arbitrary Linear Samples

ICML 2024poster

This work considers the fundamental problem of learning an unknown object from training data using a given model class. We introduce a framework that allows for objects in arbitrary Hilbert spaces, general types of (random) linear measurements as training data and general types of nonlinear model cl…

Cited by 3SourcePDFScholar
2023

CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions

NeurIPS 2023spotlight

We introduce a general framework for active learning in regression problems. Our framework extends the standard setup by allowing for general types of data, rather than merely pointwise samples of the target function. This generalization covers many cases of practical interest, such as data acquired…

Cited by 12SourcePDFScholar