Villar, Soledad

16 publications

NeurIPS 2025 On Transferring Transferability: Towards a Theory for Size Generalization Eitan Levin, Yuxin Ma, Mateo Diaz Diaz, Soledad Villar

NeurIPSW 2024 Galois Features: Nearly-Complete Invariants on Symmetric Matrices Ben Blum-Smith, Ningyuan Teresa Huang, Marco Cuturi, Soledad Villar

NeurIPS 2024 Graph Neural Networks and Non-Commuting Operators Mauricio Velasco, Kaiying O'Hare, Bernardo Rychtenberg, Soledad Villar

ICML 2024 Position: Is Machine Learning Good or Bad for the Natural Sciences? David W Hogg, Soledad Villar

ICLR 2024 Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka

TMLR 2024 Towards Fully Covariant Machine Learning Soledad Villar, David W Hogg, Weichi Yao, George A Kevrekidis, Bernhard Schölkopf

NeurIPS 2023 Approximately Equivariant Graph Networks Ningyuan Huang, Ron Levie, Soledad Villar

JMLR 2023 Dimensionless Machine Learning: Imposing Exact Units Equivariance Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu

NeurIPS 2023 Fine-Grained Expressivity of Graph Neural Networks Jan Böker, Ron Levie, Ningyuan Huang, Soledad Villar, Christopher Morris

ICMLW 2023 Learning Structured Representations with Equivariant Contrastive Learning Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka

CoLLAs 2023 Prospective Learning: Principled Extrapolation to the Future Ashwin De Silva, Rahul Ramesh, Lyle Ungar, Marshall Hussain Shuler, Noah J. Cowan, Michael Platt, Chen Li, Leyla Isik, Seung-Eon Roh, Adam Charles, Archana Venkataraman, Brian Caffo, Javier J. How, Justus M Kebschull, John W. Krakauer, Maxim Bichuch, Kaleab Alemayehu Kinfu, Eva Yezerets, Dinesh Jayaraman, Jong M. Shin, Soledad Villar, Ian Phillips, Carey E. Priebe, Thomas Hartung, Michael I. Miller, Jayanta Dey, Ningyuan Huang, Eric Eaton, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Randal Burns, Onyema Osuagwu, Brett Mensh, Alysson R. Muotri, Julia Brown, Chris White, Weiwei Yang, Andrei A. Rusu Timothy Verstynen, Konrad P. Kording, Pratik Chaudhari, Joshua T. Vogelstein

LoG 2023 The Second Learning on Graphs Conference: Preface Soledad Villar, Benjamin Chamberlain, Yuanqi Du, Hannes St"ark, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Yanqiao Zhu, Kexin Huang, Michelle Li, Sofia Bourhim, Ilia Igashov, Alexandre Duval, Mathieu Alain, Dominique Beaini, Xinyu Yuan

NeurIPSW 2022 From Local to Global: Spectral-Inspired Graph Neural Networks Ningyuan Teresa Huang, Soledad Villar, Carey Priebe, Da Zheng, Chengyue Huang, Lin Yang, Vladimir Braverman

NeurIPS 2021 Scalars Are Universal: Equivariant Machine Learning, Structured like Classical Physics Soledad Villar, David W Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith

NeurIPS 2020 Can Graph Neural Networks Count Substructures? Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna

NeurIPS 2019 On the Equivalence Between Graph Isomorphism Testing and Function Approximation with GNNs Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna