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

Yuxin Tang, Zhimin Ding, Dimitrije Jankov, Binhang Yuan, Daniel Bourgeois, Chris Jermaine
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:33581-33598, 2023.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-tang23a, title = {Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning}, author = {Tang, Yuxin and Ding, Zhimin and Jankov, Dimitrije and Yuan, Binhang and Bourgeois, Daniel and Jermaine, Chris}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {33581--33598}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/tang23a/tang23a.pdf}, url = {https://proceedings.mlr.press/v202/tang23a.html}, 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.} }
Endnote
%0 Conference Paper %T Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning %A Yuxin Tang %A Zhimin Ding %A Dimitrije Jankov %A Binhang Yuan %A Daniel Bourgeois %A Chris Jermaine %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-tang23a %I PMLR %P 33581--33598 %U https://proceedings.mlr.press/v202/tang23a.html %V 202 %X 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.
APA
Tang, Y., Ding, Z., Jankov, D., Yuan, B., Bourgeois, D. & Jermaine, C.. (2023). Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:33581-33598 Available from https://proceedings.mlr.press/v202/tang23a.html.

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