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Soledad Villar
Soledad Villar
Johns Hopkins University
Bestätigte E-Mail-Adresse bei jhu.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Can graph neural networks count substructures?
Z Chen, L Chen, S Villar, J Bruna
Advances in neural information processing systems 33, 10383-10395, 2020
3662020
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
3142019
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
1392018
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
1352015
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
1272021
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
1202017
A note on learning algorithms for quadratic assignment with graph neural networks
A Nowak, S Villar, AS Bandeira, J Bruna
stat 1050, 22, 2017
1192017
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
832021
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
782021
Probably certifiably correct k-means clustering
T Iguchi, DG Mixon, J Peterson, S Villar
Mathematical Programming, 2015
562015
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
552020
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
422019
On the tightness of an SDP relaxation of k-means
T Iguchi, DG Mixon, J Peterson, S Villar
arXiv preprint arXiv:1505.04778, 2015
422015
Machine learning and invariant theory
B Blum-Smith, S Villar
arXiv preprint arXiv:2209.14991, 2023
32*2023
Sunlayer: Stable denoising with generative networks
DG Mixon, S Villar
arXiv preprint arXiv:1803.09319, 2018
302018
Manifold optimization for k-means clustering
T Carson, DG Mixon, S Villar, R Ward
2017 International Conference on Sampling Theory and Applications (SampTA …, 2017
282017
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
262023
Fine-grained expressivity of graph neural networks
J Böker, R Levie, N Huang, S Villar, C Morris
Advances in Neural Information Processing Systems 36, 2024
242024
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
222022
MREC: a fast and versatile framework for aligning and matching point clouds with applications to single cell molecular data
AJ Blumberg, M Carriere, MA Mandell, R Rabadan, S Villar
arXiv preprint arXiv:2001.01666, 2020
202020
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