ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain

Guillermo Bernárdez; Lev Telyatnikov; Marco Montagna; Federica Baccini; Mathilde Papillon; Miquel Ferriol-Galmés; Mustafa Hajij; Theodore Papamarkou; Maria Sofia Bucarelli; Olga Zaghen; Johan Mathe; Audun Myers; Scott Mahan; Hansen Lillemark; Sharvaree Vadgama; Erik Bekkers; Tim Doster; Tegan Emerson; Henry Kvinge; Katrina Agate; Nesreen K. Ahmed; Pengfei Bai; Michael Banf; Claudio Battiloro; Maxim Beketov; Paul Bogdan; Martin Carrasco; Andrea Cavallo; Yun Young Choi; George Dasoulas; Matouš Elphick; Giordan Escalona; Dominik Filipiak; Halley Fritze; Thomas Gebhart; Manel Gil-Sorribes; Salvish Goomanee; Victor Guallar; Liliya Imasheva; Andrei Irimia; Hongwei Jin; Graham Johnson; Nikos Kanakaris; Boshko Koloski; Veljko Kovač; Manuel Lecha; Minho Lee; Pierrick Leroy; Theodore Long; German Magai; Alvaro Martinez; Marissa Masden; Sebastian Mežnar; Bertran Miquel-Oliver; Alexis Molina; Alexander Nikitin; Marco Nurisso; Matt Piekenbrock; Yu Qin; Patryk Rygiel; Alessandro Salatiello; Max Schattauer; Pavel Snopov; Julian Suk; Valentina Sánchez; Mauricio Tec; Francesco Vaccarino; Jonas Verhellen; Frederic Wantiez; Alexander Weers; Patrik Zajec; Blaž Škrlj; Nina Miolane

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain

Guillermo Bernárdez
, Lev Telyatnikov
, Marco Montagna
, Federica Baccini
, Mathilde Papillon
, Miquel Ferriol-Galmés
, Mustafa Hajij
, Theodore Papamarkou
, Maria Sofia Bucarelli
, Olga Zaghen
, Johan Mathe
, Audun Myers
, Scott Mahan
, Hansen Lillemark
, Sharvaree Vadgama
, Erik Bekkers
, Tim Doster
, Tegan Emerson
, Henry Kvinge
, Katrina Agate

Nesreen K. Ahmed, Pengfei Bai, Michael Banf, Claudio Battiloro, Maxim Beketov, Paul Bogdan, Martin Carrasco, Andrea Cavallo, Yun Young Choi, George Dasoulas, Matouš Elphick, Giordan Escalona, Dominik Filipiak, Halley Fritze, Thomas Gebhart, Manel Gil-Sorribes, Salvish Goomanee, Victor Guallar, Liliya Imasheva, Andrei Irimia, Hongwei Jin, Graham Johnson, Nikos Kanakaris, Boshko Koloski, Veljko Kovač, Manuel Lecha, Minho Lee, Pierrick Leroy, Theodore Long, German Magai, Alvaro Martinez, Marissa Masden, Sebastian Mežnar, Bertran Miquel-Oliver, Alexis Molina, Alexander Nikitin, Marco Nurisso, Matt Piekenbrock, Yu Qin, Patryk Rygiel, Alessandro Salatiello, Max Schattauer, Pavel Snopov, Julian Suk, Valentina Sánchez, Mauricio Tec, Francesco Vaccarino, Jonas Verhellen, Frederic Wantiez, Alexander Weers, Patrik Zajec, Blaž Škrlj, Nina Miolane

Strategic Management and Entrepreneurship

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2 Scopus citations

Abstract

This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem of representing data in different discrete topological domains in order to bridge the gap between Topological Deep Learning (TDL) and other types of structured datasets (e.g. point clouds, graphs). Specifically, participants were asked to design and implement topological liftings, i.e. mappings between different data structures and topological domains–like hypergraphs, or simplicial/cell/combinatorial complexes. The challenge received 52 submissions satisfying all the requirements. This paper introduces the main scope of the challenge, and summarizes the main results and findings.

Original language	English (US)
Pages (from-to)	420-428
Number of pages	9
Journal	Proceedings of Machine Learning Research
Volume	251
State	Published - 2024
Event	1st Geometry-Grounded Representation Learning and Generative Modeling Workshop, GRaM 2024 at the 41st International Conference on Machine Learning, ICML 2024 - Vienna, Austria Duration: Jul 29 2024 → …

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Copyright 2024 by the author(s).

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Bernárdez, G., Telyatnikov, L., Montagna, M., Baccini, F., Papillon, M., Ferriol-Galmés, M., Hajij, M., Papamarkou, T., Bucarelli, M. S., Zaghen, O., Mathe, J., Myers, A., Mahan, S., Lillemark, H., Vadgama, S., Bekkers, E., Doster, T., Emerson, T., Kvinge, H., ... Miolane, N. (2024). ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain. Proceedings of Machine Learning Research, 251, 420-428.

Bernárdez, G, Telyatnikov, L, Montagna, M, Baccini, F, Papillon, M, Ferriol-Galmés, M, Hajij, M, Papamarkou, T, Bucarelli, MS, Zaghen, O, Mathe, J, Myers, A, Mahan, S, Lillemark, H, Vadgama, S, Bekkers, E, Doster, T, Emerson, T, Kvinge, H, Agate, K, Ahmed, NK, Bai, P, Banf, M, Battiloro, C, Beketov, M, Bogdan, P, Carrasco, M, Cavallo, A, Choi, YY, Dasoulas, G, Elphick, M, Escalona, G, Filipiak, D, Fritze, H, Gebhart, T, Gil-Sorribes, M, Goomanee, S, Guallar, V, Imasheva, L, Irimia, A, Jin, H, Johnson, G, Kanakaris, N, Koloski, B, Kovač, V, Lecha, M, Lee, M, Leroy, P, Long, T, Magai, G, Martinez, A, Masden, M, Mežnar, S, Miquel-Oliver, B, Molina, A, Nikitin, A, Nurisso, M, Piekenbrock, M, Qin, Y, Rygiel, P, Salatiello, A, Schattauer, M, Snopov, P, Suk, J, Sánchez, V, Tec, M, Vaccarino, F, Verhellen, J, Wantiez, F, Weers, A, Zajec, P, Škrlj, B & Miolane, N 2024, 'ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain', Proceedings of Machine Learning Research, vol. 251, pp. 420-428.

@article{5d76c616e0c8488dba22790c81c57ca2,

title = "ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain",

abstract = "This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem of representing data in different discrete topological domains in order to bridge the gap between Topological Deep Learning (TDL) and other types of structured datasets (e.g. point clouds, graphs). Specifically, participants were asked to design and implement topological liftings, i.e. mappings between different data structures and topological domains–like hypergraphs, or simplicial/cell/combinatorial complexes. The challenge received 52 submissions satisfying all the requirements. This paper introduces the main scope of the challenge, and summarizes the main results and findings.",

author = "Guillermo Bern{\'a}rdez and Lev Telyatnikov and Marco Montagna and Federica Baccini and Mathilde Papillon and Miquel Ferriol-Galm{\'e}s and Mustafa Hajij and Theodore Papamarkou and Bucarelli, \{Maria Sofia\} and Olga Zaghen and Johan Mathe and Audun Myers and Scott Mahan and Hansen Lillemark and Sharvaree Vadgama and Erik Bekkers and Tim Doster and Tegan Emerson and Henry Kvinge and Katrina Agate and Ahmed, \{Nesreen K.\} and Pengfei Bai and Michael Banf and Claudio Battiloro and Maxim Beketov and Paul Bogdan and Martin Carrasco and Andrea Cavallo and Choi, \{Yun Young\} and George Dasoulas and Matou{\v s} Elphick and Giordan Escalona and Dominik Filipiak and Halley Fritze and Thomas Gebhart and Manel Gil-Sorribes and Salvish Goomanee and Victor Guallar and Liliya Imasheva and Andrei Irimia and Hongwei Jin and Graham Johnson and Nikos Kanakaris and Boshko Koloski and Veljko Kova{\v c} and Manuel Lecha and Minho Lee and Pierrick Leroy and Theodore Long and German Magai and Alvaro Martinez and Marissa Masden and Sebastian Me{\v z}nar and Bertran Miquel-Oliver and Alexis Molina and Alexander Nikitin and Marco Nurisso and Matt Piekenbrock and Yu Qin and Patryk Rygiel and Alessandro Salatiello and Max Schattauer and Pavel Snopov and Julian Suk and Valentina S{\'a}nchez and Mauricio Tec and Francesco Vaccarino and Jonas Verhellen and Frederic Wantiez and Alexander Weers and Patrik Zajec and Bla{\v z} {\v S}krlj and Nina Miolane",

note = "Publisher Copyright: Copyright 2024 by the author(s).; 1st Geometry-Grounded Representation Learning and Generative Modeling Workshop, GRaM 2024 at the 41st International Conference on Machine Learning, ICML 2024 ; Conference date: 29-07-2024",

year = "2024",

language = "English (US)",

volume = "251",

pages = "420--428",

journal = "Proceedings of Machine Learning Research",

issn = "2640-3498",

publisher = "ML Research Press",

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T1 - ICML Topological Deep Learning Challenge 2024

T2 - 1st Geometry-Grounded Representation Learning and Generative Modeling Workshop, GRaM 2024 at the 41st International Conference on Machine Learning, ICML 2024

AU - Bernárdez, Guillermo

AU - Telyatnikov, Lev

AU - Montagna, Marco

AU - Baccini, Federica

AU - Papillon, Mathilde

AU - Ferriol-Galmés, Miquel

AU - Hajij, Mustafa

AU - Papamarkou, Theodore

AU - Bucarelli, Maria Sofia

AU - Zaghen, Olga

AU - Mathe, Johan

AU - Myers, Audun

AU - Mahan, Scott

AU - Lillemark, Hansen

AU - Vadgama, Sharvaree

AU - Bekkers, Erik

AU - Doster, Tim

AU - Emerson, Tegan

AU - Kvinge, Henry

AU - Agate, Katrina

AU - Ahmed, Nesreen K.

AU - Bai, Pengfei

AU - Banf, Michael

AU - Battiloro, Claudio

AU - Beketov, Maxim

AU - Bogdan, Paul

AU - Carrasco, Martin

AU - Cavallo, Andrea

AU - Choi, Yun Young

AU - Dasoulas, George

AU - Elphick, Matouš

AU - Escalona, Giordan

AU - Filipiak, Dominik

AU - Fritze, Halley

AU - Gebhart, Thomas

AU - Gil-Sorribes, Manel

AU - Goomanee, Salvish

AU - Guallar, Victor

AU - Imasheva, Liliya

AU - Irimia, Andrei

AU - Jin, Hongwei

AU - Johnson, Graham

AU - Kanakaris, Nikos

AU - Koloski, Boshko

AU - Kovač, Veljko

AU - Lecha, Manuel

AU - Lee, Minho

AU - Leroy, Pierrick

AU - Long, Theodore

AU - Magai, German

AU - Martinez, Alvaro

AU - Masden, Marissa

AU - Mežnar, Sebastian

AU - Miquel-Oliver, Bertran

AU - Molina, Alexis

AU - Nikitin, Alexander

AU - Nurisso, Marco

AU - Piekenbrock, Matt

AU - Qin, Yu

AU - Rygiel, Patryk

AU - Salatiello, Alessandro

AU - Schattauer, Max

AU - Snopov, Pavel

AU - Suk, Julian

AU - Sánchez, Valentina

AU - Tec, Mauricio

AU - Vaccarino, Francesco

AU - Verhellen, Jonas

AU - Wantiez, Frederic

AU - Weers, Alexander

AU - Zajec, Patrik

AU - Škrlj, Blaž

AU - Miolane, Nina

PY - 2024

Y1 - 2024

N2 - This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem of representing data in different discrete topological domains in order to bridge the gap between Topological Deep Learning (TDL) and other types of structured datasets (e.g. point clouds, graphs). Specifically, participants were asked to design and implement topological liftings, i.e. mappings between different data structures and topological domains–like hypergraphs, or simplicial/cell/combinatorial complexes. The challenge received 52 submissions satisfying all the requirements. This paper introduces the main scope of the challenge, and summarizes the main results and findings.

AB - This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem of representing data in different discrete topological domains in order to bridge the gap between Topological Deep Learning (TDL) and other types of structured datasets (e.g. point clouds, graphs). Specifically, participants were asked to design and implement topological liftings, i.e. mappings between different data structures and topological domains–like hypergraphs, or simplicial/cell/combinatorial complexes. The challenge received 52 submissions satisfying all the requirements. This paper introduces the main scope of the challenge, and summarizes the main results and findings.

UR - https://www.scopus.com/pages/publications/85216611937

UR - https://www.scopus.com/pages/publications/85216611937#tab=citedBy

M3 - Conference article

AN - SCOPUS:85216611937

SN - 2640-3498

VL - 251

SP - 420

EP - 428

JO - Proceedings of Machine Learning Research

JF - Proceedings of Machine Learning Research

Y2 - 29 July 2024

ER -

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain

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