Soledad Villar

According to our database, Soledad Villar authored at least 38 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Dimensionless machine learning: Imposing exact units equivariance.

Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu

J. Mach. Learn. Res., 2023

Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning.

Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka

CoRR, 2023

GeometricImageNet: Extending convolutional neural networks to vector and tensor images.

Wilson Gregory, David W. Hogg, Ben Blum-Smith, Maria Teresa Arias, Kaze W. K. Wong, Soledad Villar

CoRR, 2023

The passive symmetries of machine learning.

Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf

CoRR, 2023

Approximately Equivariant Graph Networks.

Ningyuan Huang, Ron Levie, Soledad Villar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fine-grained Expressivity of Graph Neural Networks.

Jan Böker, Ron Levie, Ningyuan Huang, Soledad Villar, Christopher Morris

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Prospective Learning: Principled Extrapolation to the Future.

Ashwin De Silva, Rahul Ramesh, Lyle H. Ungar, Marshall G. 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 C. Burns, Onyema Osuagwu, Brett Mensh, Alysson R. Muotri, Julia Brown, Chris White, Weiwei Yang, Andrei A. Rusu, Timothy D. Verstynen, Konrad P. Kording, Pratik Chaudhari, Joshua T. Vogelstein

Proceedings of the Conference on Lifelong Learning Agents, 2023

2022

Dimensionality Reduction, Regularization, and Generalization in Overparameterized Regressions.

Ningyuan Teresa Huang, David W. Hogg, Soledad Villar

SIAM J. Math. Data Sci., 2022

Sketch-and-solve approaches to k-means clustering by semidefinite programming.

Charles Clum, Dustin G. Mixon, Soledad Villar, Kaiying Xie

CoRR, 2022

Graph Neural Networks for Community Detection on Sparse Graphs.

Luana Ruiz, Ningyuan Huang, Soledad Villar

CoRR, 2022

Deep Learning is Provably Robust to Symmetric Label Noise.

Carey E. Priebe, Ningyuan Huang, Soledad Villar, Cong Mu, Li Chen

CoRR, 2022

Shuffled linear regression through graduated convex relaxation.

Efe Onaran, Soledad Villar

CoRR, 2022

Equivariant maps from invariant functions.

Ben Blum-Smith, Soledad Villar

CoRR, 2022

From Local to Global: Spectral-Inspired Graph Neural Networks.

Ningyuan Huang, Soledad Villar, Carey E. Priebe, Da Zheng, Chengyue Huang, Lin Yang, Vladimir Braverman

CoRR, 2022

MarkerMap: nonlinear marker selection for single-cell studies.

Nabeel Sarwar, Wilson Gregory, George A. Kevrekidis, Soledad Villar, Bianca Dumitrascu

CoRR, 2022

2021

A simple equivariant machine learning method for dynamics based on scalars.

Weichi Yao, Kate Storey-Fisher, David W. Hogg, Soledad Villar

CoRR, 2021

Scalars are universal: Gauge-equivariant machine learning, structured like classical physics.

Soledad Villar, David W. Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith

CoRR, 2021

Fitting very flexible models: Linear regression with large numbers of parameters.

David W. Hogg, Soledad Villar

CoRR, 2021

Scalars are universal: Equivariant machine learning, structured like classical physics.

Soledad Villar, David W. Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants.

Ningyuan Teresa Huang, Soledad Villar

Proceedings of the IEEE International Conference on Acoustics, 2021

2020

SqueezeFit: Label-Aware Dimensionality Reduction by Semidefinite Programming.

Culver McWhirter, Dustin G. Mixon, Soledad Villar

IEEE Trans. Inf. Theory, 2020

MREC: a fast and versatile framework for aligning and matching point clouds with applications to single cell molecular data.

Andrew J. Blumberg, Mathieu Carrière, Michael A. Mandell, Raul Rabadan, Soledad Villar

CoRR, 2020

Can Graph Neural Networks Count Substructures?

Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient Belief Propagation for Graph Matching.

Efe Onaran, Soledad Villar

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019

Fair redistricting is hard.

Richard Kueng, Dustin G. Mixon, Soledad Villar

Theor. Comput. Sci., 2019

Experimental performance of graph neural networks on random instances of max-cut.

Weichi Yao, Afonso S. Bandeira, Soledad Villar

CoRR, 2019

On the equivalence between graph isomorphism testing and function approximation with GNNs.

Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Utility Ghost: Gamified redistricting with partisan symmetry.

Dustin G. Mixon, Soledad Villar

CoRR, 2018

SUNLayer: Stable denoising with generative networks.

Dustin G. Mixon, Soledad Villar

CoRR, 2018

Revised Note on Learning Quadratic Assignment with Graph Neural Networks.

Alex Nowak, Soledad Villar, Afonso S. Bandeira, Joan Bruna

Proceedings of the 2018 IEEE Data Science Workshop, 2018

2017

Probably certifiably correct k-means clustering.

Takayuki Iguchi, Dustin G. Mixon, Jesse Peterson, Soledad Villar

Math. Program., 2017

Projected Power Iteration for Network Alignment.

Efe Onaran, Soledad Villar

CoRR, 2017

A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks.

Alex Nowak, Soledad Villar, Afonso S. Bandeira, Joan Bruna

CoRR, 2017

2016

A polynomial-time relaxation of the Gromov-Hausdorff distance.

Soledad Villar, Afonso S. Bandeira, Andrew J. Blumberg, Rachel A. Ward

CoRR, 2016

Clustering subgaussian mixtures by semidefinite programming.

Dustin G. Mixon, Soledad Villar, Rachel A. Ward

CoRR, 2016

Clustering subgaussian mixtures with k-means.

Dustin G. Mixon, Soledad Villar, Rachel A. Ward

Proceedings of the 2016 IEEE Information Theory Workshop, 2016

2015

On the tightness of an SDP relaxation of k-means.

Takayuki Iguchi, Dustin G. Mixon, Jesse Peterson, Soledad Villar

CoRR, 2015

Relax, No Need to Round: Integrality of Clustering Formulations.

Pranjal Awasthi, Afonso S. Bandeira, Moses Charikar, Ravishankar Krishnaswamy, Soledad Villar, Rachel A. Ward

Proceedings of the 2015 Conference on Innovations in Theoretical Computer Science, 2015