Soledad Villar

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Dustin G. MixonAssociate Professor, Department of Mathematics, The Ohio State UniversityVerified email at osu.edu
Joan BrunaCourant Institute of Mathematical Sciences, New York UniversityVerified email at cims.nyu.edu
Afonso S. BandeiraProfessor of Mathematics, ETH ZurichVerified email at math.ethz.ch
Rachel WardUT AustinVerified email at math.utexas.edu
David W HoggNew York University & MPIA & Center for Computational Astrophysics, Flatiron InstituteVerified email at nyu.edu
Zhengdao ChenNew York UniversityVerified email at cims.nyu.edu
Weichi YaoPh.D. candidate, New York UniversityVerified email at nyu.edu
Alex Nowak-VilaINRIA - École Normale SupérieureVerified email at inria.fr
Benjamin Blum-SmithJohns Hopkins UniversityVerified email at newschool.edu
Jesse PetersonUnited States Air ForceVerified email at us.af.mil
Takayuki IguchiAir Force Institute of TechnologyVerified email at au.af.edu
Bianca DumitrascuCambridge UniversityVerified email at cam.ac.uk
Lei ChenNew York UniversityVerified email at nyu.edu
Ravishankar KrishnaswamyMicrosoft ResearchVerified email at cs.cmu.edu
pranjal awasthiRutgers UniversityVerified email at rutgers.edu
Moses CharikarProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Ningyuan (Teresa) HuangJohns Hopkins UniversityVerified email at jhu.edu
Zoi-Heleni MichalopoulouNew Jersey Institute of TechnologyVerified email at njit.edu
Andrew J. BlumbergProfessor of mathematics and computer science, Columbia UniversityVerified email at math.columbia.edu
Efe OnaranTechnion - Israel Institute of TechnologyVerified email at campus.technion.ac.il

Soledad Villar

Johns Hopkins University

Verified email at jhu.edu - Homepage

machine learning data science optimization


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	165	2019
Can graph neural networks count substructures? Z Chen, L Chen, S Villar, J Bruna Advances in neural information processing systems 33, 10383-10395, 2020	138	2020
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	123	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	106	2017
A note on learning algorithms for quadratic assignment with graph neural networks A Nowak, S Villar, AS Bandeira, J Bruna stat 1050, 22, 2017	79	2017
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	78	2018
Probably certifiably correct k-means clustering T Iguchi, DG Mixon, J Peterson, S Villar Mathematical Programming, 2015	48	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	37	2015
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	35	2021
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	33	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	29	2020
Sunlayer: Stable denoising with generative networks DG Mixon, S Villar arXiv preprint arXiv:1803.09319, 2018	27	2018
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	22	2019
Manifold optimization for k-means clustering T Carson, DG Mixon, S Villar 2017 International Conference on Sampling Theory and Applications (SampTA …, 2017	19	2017
A polynomial-time relaxation of the Gromov-Hausdorff distance S Villar, AS Bandeira, AJ Blumberg, R Ward arXiv preprint arXiv:1610.05214, 2016	17	2016
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	13	2021
Fair redistricting is hard R Kueng, DG Mixon, S Villar Theoretical Computer Science 791, 28-35, 2019	13	2019
Clustering subgaussian mixtures with k-means DG Mixon, S Villar, R Ward Information Theory Workshop (ITW), 2016 IEEE, 211-215, 2016	11	2016
Squeezefit: Label-aware dimensionality reduction by semidefinite programming C McWhirter, DG Mixon, S Villar IEEE Transactions on Information Theory 66 (6), 3878-3892, 2019	9	2019
Projected power iteration for network alignment E Onaran, S Villar Wavelets and Sparsity XVII 10394, 298-305, 2017	9	2017

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