Soledad Villar

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i10-index	30	28

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Dustin G. MixonAssociate Professor, Department of Mathematics, The Ohio State UniversityVerified email at osu.edu
David W HoggNew York University & MPIA & Center for Computational Astrophysics, Flatiron InstituteVerified email at nyu.edu
Benjamin Blum-SmithJohns Hopkins UniversityVerified email at jhu.edu
Ningyuan (Teresa) HuangFlatiron InstituteVerified email at flatironinstitute.org
Weichi YaoAI in Science Postdoctral Research Fellows, University of MichiganVerified email at umich.edu
Rachel WardUT Austin / Microsoft ResearchVerified email at math.utexas.edu
Joan BrunaProfessor of Computer Science, Data Science & Mathematics (aff), Courant Institute and CDS, NYUVerified email at cims.nyu.edu
Afonso S. BandeiraProfessor of Mathematics, ETH ZurichVerified email at math.ethz.ch
Zhengdao ChenGoogle ResearchVerified email at google.com
Bianca M. DumitrascuAssistant Professor of Statistics, Columbia UniversityVerified email at columbia.edu
Alex Nowak-VilaOwkin Inc.Verified email at inria.fr
George A KevrekidisGraduate Student, Johns Hopkins UniversityVerified email at jh.edu
Jesse PetersonUnited States Air ForceVerified email at us.af.mil
Takayuki IguchiAir Force Institute of TechnologyVerified email at au.af.edu
Ron LevieFaculty of Mathematics, Technion – Israel Institute of TechnologyVerified email at technion.ac.il
Ravishankar KrishnaswamyMicrosoft ResearchVerified email at cs.cmu.edu
pranjal awasthiGoogleVerified email at rutgers.edu
Moses CharikarProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Zoi-Heleni MichalopoulouNew Jersey Institute of TechnologyVerified email at njit.edu
Wilson GregoryPhD Student, Johns Hopkins UniversityVerified email at jhu.edu

Soledad Villar

Johns Hopkins University

Verified email at jhu.edu - Homepage

mathematics of data geometric deep learning computational harmonic analysis


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Can graph neural networks count substructures? Z Chen, L Chen, S Villar, J Bruna Advances in neural information processing systems 33, 10383-10395, 2020	392	2020
On the equivalence between graph isomorphism testing and function approximation with gnns Z Chen, S Villar, L Chen, J Bruna Advances in neural information processing systems 32, 2019	334	2019
Revised note on learning quadratic assignment with graph neural networks A Nowak, S Villar, AS Bandeira, J Bruna 2018 IEEE Data Science Workshop (DSW), 1-5, 2018	255	2018
Scalars are universal: Equivariant machine learning, structured like classical physics S Villar, DW Hogg, K Storey-Fisher, W Yao, B Blum-Smith Advances in Neural Information Processing Systems 34, 28848-28863, 2021	148	2021
Relax, no need to round: Integrality of clustering formulations P Awasthi, AS Bandeira, M Charikar, R Krishnaswamy, S Villar, R Ward Proceedings of the 2015 Conference on Innovations in Theoretical Computer …, 2015	139	2015
Clustering subgaussian mixtures by semidefinite programming DG Mixon, S Villar, R Ward Information and Inference: A Journal of the IMA 6 (4), 389-415, 2017	123	2017
A short tutorial on the weisfeiler-lehman test and its variants NT Huang, S Villar ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021	97	2021
Optimal marker gene selection for cell type discrimination in single cell analyses B Dumitrascu, S Villar, DG Mixon, BE Engelhardt Nature communications 12 (1), 1186, 2021	86	2021
Seabed classification using physics-based modeling and machine learning C Frederick, S Villar, ZH Michalopoulou The Journal of the Acoustical Society of America 148 (2), 859-872, 2020	57	2020
Probably certifiably correct k-means clustering T Iguchi, DG Mixon, J Peterson, S Villar Mathematical Programming, 2015	56	2015
On the tightness of an SDP relaxation of k-means T Iguchi, DG Mixon, J Peterson, S Villar arXiv preprint arXiv:1505.04778, 2015	43	2015
Experimental performance of graph neural networks on random instances of max-cut W Yao, AS Bandeira, S Villar Wavelets and Sparsity XVIII 11138, 242-251, 2019	42	2019
Fine-grained expressivity of graph neural networks J Böker, R Levie, N Huang, S Villar, C Morris Advances in Neural Information Processing Systems 36, 46658-46700, 2023	31	2023
Manifold optimization for k-means clustering T Carson, DG Mixon, S Villar, R Ward 2017 International Conference on Sampling Theory and Applications (SampTA …, 2017	31	2017
Sunlayer: Stable denoising with generative networks DG Mixon, S Villar arXiv preprint arXiv:1803.09319, 2018	30	2018
Dimensionless machine learning: Imposing exact units equivariance S Villar, W Yao, DW Hogg, B Blum-Smith, B Dumitrascu Journal of Machine Learning Research 24 (109), 1-32, 2023	28	2023
Machine learning and invariant theory B Blum-Smith, S Villar arXiv preprint arXiv:2209.14991, 2023	24	2023
Dimensionality reduction, regularization, and generalization in overparameterized regressions N Teresa, DW Hogg, S Villar SIAM Journal on Mathematics of Data Science 4 (1), 126-152, 2022	24	2022
Towards fully covariant machine learning S Villar, DW Hogg, W Yao, GA Kevrekidis, B Schölkopf arXiv preprint arXiv:2301.13724, 2023	20*	2023
Approximately equivariant graph networks N Huang, R Levie, S Villar Advances in Neural Information Processing Systems 36, 34627-34660, 2023	19	2023

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