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A Unified Approach to Coreset Learning by Maalouf, Alaa; Eini, Gilad; Mussay, Ben; Feldman, Dan; Osadchy, Margarita is a scholarly article available to read on EtoBox.

What is A Unified Approach to Coreset Learning about?

Coreset of a given dataset and loss function is usually a small weighed set that approximates this loss for every query from a given set of queries. Coresets have shown to be very useful in many applications. However, coresets construction is done in a problem dependent manner and it could take years to design and prove the correctness of a coreset for a specific family of queries. This could limit coresets use in practical applications. Moreover, small coresets provably do not exist for many problems. To address these limitations, we propose a generic, learning-based algorithm for construction of coresets. Our approach offers a new definition of coreset, which is a natural relaxation of the standard definition and aims at approximating the \emph{average} loss of the original data over the queries. This allows us to use a learning paradigm to compute a small coreset of a given set of inputs with respect to a given loss function using a training set of queries. We derive formal guarantees for the proposed approach. Experimental evaluation on deep networks and classic machine learning problems show that our learned coresets yield comparable or even better results than the existing al

Author

Maalouf, Alaa; Eini, Gilad; Mussay, Ben; Feldman, Dan; Osadchy, Margarita

Published

2021

Language

EN