Preface

Timothy Doster, Tegan Emerson, Henry Kvinge, Nina Miolane, Mathilde Papillon, Bastian Rieck, Sophia Sanborn
Proceedings of 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML), PMLR 221:1-2, 2023.

Abstract

The 2nd ICML Workshop on Topology, Algebra, and Geometry in Machine Learning is an exercise in bringing together researchers working in both of the above threads to exchange ideas, present recent work, and form new collaborations. This proceedings collection captures some of the rich flow of ideas that happened in the workshop. It is also a testament to the breadth of ways that mathematics is currently being applied to modern ML, from the topology of models and datasets to equivariance of models to group actions to applications of hyperbolic geometry to learning problems.

Cite this Paper

BibTeX
@InProceedings{pmlr-v221-doster23a, title = {Preface}, author = {Doster, Timothy and Emerson, Tegan and Kvinge, Henry and Miolane, Nina and Papillon, Mathilde and Rieck, Bastian and Sanborn, Sophia}, booktitle = {Proceedings of 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML)}, pages = {1--2}, year = {2023}, editor = {Doster, Timothy and Emerson, Tegan and Kvinge, Henry and Miolane, Nina and Papillon, Mathilde and Rieck, Bastian and Sanborn, Sophia}, volume = {221}, series = {Proceedings of Machine Learning Research}, month = {28 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v221/doster23a/doster23a.pdf}, url = {https://proceedings.mlr.press/v221/doster23a.html}, abstract = {The 2nd ICML Workshop on Topology, Algebra, and Geometry in Machine Learning is an exercise in bringing together researchers working in both of the above threads to exchange ideas, present recent work, and form new collaborations. This proceedings collection captures some of the rich flow of ideas that happened in the workshop. It is also a testament to the breadth of ways that mathematics is currently being applied to modern ML, from the topology of models and datasets to equivariance of models to group actions to applications of hyperbolic geometry to learning problems.} }
Endnote
%0 Conference Paper %T Preface %A Timothy Doster %A Tegan Emerson %A Henry Kvinge %A Nina Miolane %A Mathilde Papillon %A Bastian Rieck %A Sophia Sanborn %B Proceedings of 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML) %C Proceedings of Machine Learning Research %D 2023 %E Timothy Doster %E Tegan Emerson %E Henry Kvinge %E Nina Miolane %E Mathilde Papillon %E Bastian Rieck %E Sophia Sanborn %F pmlr-v221-doster23a %I PMLR %P 1--2 %U https://proceedings.mlr.press/v221/doster23a.html %V 221 %X The 2nd ICML Workshop on Topology, Algebra, and Geometry in Machine Learning is an exercise in bringing together researchers working in both of the above threads to exchange ideas, present recent work, and form new collaborations. This proceedings collection captures some of the rich flow of ideas that happened in the workshop. It is also a testament to the breadth of ways that mathematics is currently being applied to modern ML, from the topology of models and datasets to equivariance of models to group actions to applications of hyperbolic geometry to learning problems.
APA
Doster, T., Emerson, T., Kvinge, H., Miolane, N., Papillon, M., Rieck, B. & Sanborn, S.. (2023). Preface. Proceedings of 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML), in Proceedings of Machine Learning Research 221:1-2 Available from https://proceedings.mlr.press/v221/doster23a.html.

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