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A Unified Approach to Coreset Learning.

Alaa Maalouf, Gilad Eini, Ben Mussay

IEEE Transactions on Neural Networks and Learning Systems

|November 7, 2022

Summary

This summary is machine-generated.

This study introduces a novel learning-based algorithm for constructing coresets, which are small, weighted datasets that approximate data loss. This approach offers a more practical and efficient method for coreset generation across various machine learning applications.

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Area of Science:

Machine Learning
Data Science
Algorithm Development

Background:

Coresets are valuable for approximating data loss but traditional construction methods are problem-specific and time-consuming.
Existing coreset construction can be inefficient and theoretically limited, hindering practical applications.
Small coresets are not always feasible for all problems, necessitating alternative approaches.

Purpose of the Study:

To develop a generic, learning-based algorithm for constructing coresets.
To introduce a relaxed definition of coresets that approximates average data loss.
To enable efficient coreset computation using a training set of queries.

Main Methods:

A novel, learning-based algorithm for coreset construction is proposed.
The algorithm utilizes a training set of queries to compute coresets for a given loss function.
Formal guarantees for the learning-based approach are derived.

Main Results:

Learned coresets demonstrate comparable or superior performance to existing methods on deep networks and classic machine learning tasks.
The approach provides the first coreset for entire deep networks, not just individual layers.
Experimental results validate the effectiveness and efficiency of the proposed learning-based coreset construction.

Conclusions:

The proposed learning-based algorithm offers a practical and effective solution for coreset construction.
This method overcomes limitations of traditional, problem-dependent coreset design.
The ability to create coresets for full deep networks represents a significant advancement in model compression.