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2023 ICML ICML 2023

Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning

Abstract

The relational data model was designed to facilitate large-scale data management and analytics. We consider the problem of how to differentiate computations expressed relationally. We show experimentally that a relational engine running an auto-differentiated relational algorithm can easily scale to very large datasets, and is competitive with state-of-the-art, special-purpose systems for large-scale distributed machine learning.

🧭 Keyword Pioneer — relational computation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Machine Learning, Mathematics & Optimization
🌉 Interdisciplinary Bridge — Computer Science and Deep Learning and Machine Learning

Authors

Yuxin Tang , Zhimin Ding , Dimitrije Jankov , Binhang Yuan , Daniel Bourgeois , Chris Jermaine

Topics

Machine Learning > Core Methods > Representation Learning Machine Learning > Optimization & Theory > Distributed Learning Machine Learning > Optimization & Theory > Optimization Machine Learning > Application Areas > Efficient Computing Computer Science > Systems > Databases Deep Learning > Optimization & Theory > Optimization

Keywords

distributed machine learning relational database large-scale machine learning scalable algorithm relational computation
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