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Soledad Villar
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2020 – today
- 2024
- [j6]Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf:
Towards fully covariant machine learning. Trans. Mach. Learn. Res. 2024 (2024) - [c15]Luana Ruiz, Ningyuan Teresa Huang, Soledad Villar:
A Spectral Analysis of Graph Neural Networks on Dense and Sparse Graphs. ICASSP 2024: 9936-9940 - [c14]Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka:
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning. ICLR 2024 - [c13]David W. Hogg, Soledad Villar:
Position: Is machine learning good or bad for the natural sciences? ICML 2024 - [i37]Ben Blum-Smith, Ningyuan Huang, Marco Cuturi, Soledad Villar:
Learning functions on symmetric matrices and point clouds via lightweight invariant features. CoRR abs/2405.08097 (2024) - [i36]David W. Hogg, Soledad Villar:
Is machine learning good or bad for the natural sciences? CoRR abs/2405.18095 (2024) - [i35]Wilson Gregory, Josué Tonelli-Cueto, Nicholas F. Marshall, Andrew S. Lee
, Soledad Villar:
Learning equivariant tensor functions with applications to sparse vector recovery. CoRR abs/2406.01552 (2024) - [i34]George A. Kevrekidis, Mauro Maggioni, Soledad Villar, Yannis G. Kevrekidis:
Thinner Latent Spaces: Detecting dimension and imposing invariance through autoencoder gradient constraints. CoRR abs/2408.16138 (2024) - 2023
- [j5]Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu:
Dimensionless machine learning: Imposing exact units equivariance. J. Mach. Learn. Res. 24: 109:1-109:32 (2023) - [c12]Ashwin De Silva, Rahul Ramesh, Lyle H. Ungar, Marshall G. Hussain Shuler, Noah J. Cowan, Michael L. 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
Prospective Learning: Principled Extrapolation to the Future. CoLLAs 2023: 347-357 - [c11]Soledad Villar, Benjamin Paul Chamberlain, Yuanqi Du, Hannes Stärk, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov, Alexandre Duval, Mathieu Alain, Dominique Beaini, Xinyu Yuan:
The Second Learning on Graphs Conference: Preface. LoG 2023: i-xix - [c10]Jan Böker, Ron Levie, Ningyuan Huang, Soledad Villar, Christopher Morris:
Fine-grained Expressivity of Graph Neural Networks. NeurIPS 2023 - [c9]Ningyuan Huang, Ron Levie, Soledad Villar:
Approximately Equivariant Graph Networks. NeurIPS 2023 - [e1]Soledad Villar, Benjamin Chamberlain:
Learning on Graphs Conference, 27-30 November 2023, Virtual Event. Proceedings of Machine Learning Research 231, PMLR 2023 [contents] - [i33]Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf:
The passive symmetries of machine learning. CoRR abs/2301.13724 (2023) - [i32]Wilson Gregory, David W. Hogg, Ben Blum-Smith, Maria Teresa Arias
GeometricImageNet: Extending convolutional neural networks to vector and tensor images. CoRR abs/2305.12585 (2023) - [i31]Jan Böker, Ron Levie, Ningyuan Huang, Soledad Villar, Christopher Morris:
Fine-grained Expressivity of Graph Neural Networks. CoRR abs/2306.03698 (2023) - [i30]Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka:
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning. CoRR abs/2306.13924 (2023) - [i29]Ningyuan Huang, Ron Levie, Soledad Villar:
Approximately Equivariant Graph Networks. CoRR abs/2308.10436 (2023) - 2022
- [j4]Ningyuan Teresa Huang, David W. Hogg, Soledad Villar:
Dimensionality Reduction, Regularization, and Generalization in Overparameterized Regressions. SIAM J. Math. Data Sci. 4(1): 126-152 (2022) - [i28]Ningyuan Huang, Soledad Villar:
A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants. CoRR abs/2201.07083 (2022) - [i27]Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu
Dimensionless machine learning: Imposing exact units equivariance. CoRR abs/2204.00887 (2022) - [i26]Nabeel Sarwar, Wilson Gregory, George A. Kevrekidis, Soledad Villar, Bianca Dumitrascu:
MarkerMap: nonlinear marker selection for single-cell studies. CoRR abs/2207.14106 (2022) - [i25]Ningyuan Huang, Soledad Villar, Carey E. Priebe, Da Zheng, Chengyue Huang, Lin Yang
From Local to Global: Spectral-Inspired Graph Neural Networks. CoRR abs/2209.12054 (2022) - [i24]Ben Blum-Smith, Soledad Villar:
Equivariant maps from invariant functions. CoRR abs/2209.14991 (2022) - [i23]Efe Onaran, Soledad Villar:
Shuffled linear regression through graduated convex relaxation. CoRR abs/2209.15608 (2022) - [i22]Carey E. Priebe, Ningyuan Huang, Soledad Villar, Cong Mu, Li Chen:
Deep Learning is Provably Robust to Symmetric Label Noise. CoRR abs/2210.15083 (2022) - [i21]Luana Ruiz, Ningyuan Huang, Soledad Villar:
Graph Neural Networks for Community Detection on Sparse Graphs. CoRR abs/2211.03231 (2022) - [i20]Charles Clum, Dustin G. Mixon, Soledad Villar, Kaiying Xie:
Sketch-and-solve approaches to k-means clustering by semidefinite programming. CoRR abs/2211.15744 (2022) - 2021
- [c8]Ningyuan Teresa Huang, Soledad Villar:
A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants. ICASSP 2021: 8533-8537 - [c7]Soledad Villar, David W. Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith:
Scalars are universal: Equivariant machine learning, structured like classical physics. NeurIPS 2021: 28848-28863 - [i19]David W. Hogg, Soledad Villar:
Fitting very flexible models: Linear regression with large numbers of parameters. CoRR abs/2101.07256 (2021) - [i18]Soledad Villar, David W. Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith:
Scalars are universal: Gauge-equivariant machine learning, structured like classical physics. CoRR abs/2106.06610 (2021) - [i17]Weichi Yao, Kate Storey-Fisher, David W. Hogg, Soledad Villar:
A simple equivariant machine learning method for dynamics based on scalars. CoRR abs/2110.03761 (2021) - 2020
- [j3]Culver McWhirter, Dustin G. Mixon, Soledad Villar
SqueezeFit: Label-Aware Dimensionality Reduction by Semidefinite Programming. IEEE Trans. Inf. Theory 66(6): 3878-3892 (2020) - [c6]Efe Onaran, Soledad Villar:
Efficient Belief Propagation for Graph Matching. ICASSP 2020: 9060-9064 - [c5]Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna:
Can Graph Neural Networks Count Substructures? NeurIPS 2020 - [i16]Andrew J. Blumberg, Mathieu Carrière, Michael A. Mandell, Raul Rabadan, Soledad Villar:
MREC: a fast and versatile framework for aligning and matching point clouds with applications to single cell molecular data. CoRR abs/2001.01666 (2020) - [i15]Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna:
Can graph neural networks count substructures? CoRR abs/2002.04025 (2020) - [i14]Ningyuan Huang, David W. Hogg, Soledad Villar:
Dimensionality reduction, regularization, and generalization in overparameterized regressions. CoRR abs/2011.11477 (2020)
2010 – 2019
- 2019
- [j2]Richard Kueng
Fair redistricting is hard. Theor. Comput. Sci. 791: 28-35 (2019) - [c4]Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna:
On the equivalence between graph isomorphism testing and function approximation with GNNs. NeurIPS 2019: 15868-15876 - [i13]Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna:
On the equivalence between graph isomorphism testing and function approximation with GNNs. CoRR abs/1905.12560 (2019) - [i12]Weichi Yao, Afonso S. Bandeira, Soledad Villar:
Experimental performance of graph neural networks on random instances of max-cut. CoRR abs/1908.05767 (2019) - 2018
- [c3]Alex Nowak, Soledad Villar, Afonso S. Bandeira, Joan Bruna:
Revised Note on Learning Quadratic Assignment with Graph Neural Networks. DSW 2018: 229-233 - [i11]Dustin G. Mixon, Soledad Villar:
SUNLayer: Stable denoising with generative networks. CoRR abs/1803.09319 (2018) - [i10]Richard Kueng, Dustin G. Mixon, Soledad Villar:
Fair redistricting is hard. CoRR abs/1808.08905 (2018) - [i9]Culver McWhirter, Dustin G. Mixon, Soledad Villar:
SqueezeFit: Label-aware dimensionality reduction by semidefinite programming. CoRR abs/1812.02768 (2018) - [i8]Dustin G. Mixon, Soledad Villar:
Utility Ghost: Gamified redistricting with partisan symmetry. CoRR abs/1812.07377 (2018) - 2017
- [j1]Takayuki Iguchi
Probably certifiably correct k-means clustering. Math. Program. 165(2): 605-642 (2017) - [i7]Alex Nowak, Soledad Villar, Afonso S. Bandeira, Joan Bruna:
A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks. CoRR abs/1706.07450 (2017) - [i6]Efe Onaran, Soledad Villar:
Projected Power Iteration for Network Alignment. CoRR abs/1707.04929 (2017) - 2016
- [c2]Dustin G. Mixon, Soledad Villar, Rachel A. Ward
Clustering subgaussian mixtures with k-means. ITW 2016: 211-215 - [i5]Dustin G. Mixon, Soledad Villar, Rachel A. Ward:
Clustering subgaussian mixtures by semidefinite programming. CoRR abs/1602.06612 (2016) - [i4]Soledad Villar, Afonso S. Bandeira, Andrew J. Blumberg, Rachel A. Ward:
A polynomial-time relaxation of the Gromov-Hausdorff distance. CoRR abs/1610.05214 (2016) - 2015
- [c1]Pranjal Awasthi, Afonso S. Bandeira, Moses Charikar
Relax, No Need to Round: Integrality of Clustering Formulations. ITCS 2015: 191-200 - [i3]Takayuki Iguchi, Dustin G. Mixon, Jesse Peterson, Soledad Villar:
On the tightness of an SDP relaxation of k-means. CoRR abs/1505.04778 (2015) - [i2]Takayuki Iguchi
Probably certifiably correct k-means clustering. CoRR abs/1509.07983 (2015) - 2014
- [i1]Pranjal Awasthi, Afonso S. Bandeira, Moses Charikar, Ravishankar Krishnaswamy, Soledad Villar, Rachel A. Ward:
Relax, no need to round: integrality of clustering formulations. CoRR abs/1408.4045 (2014)
Coauthor Index
aka: Ningyuan Teresa Huang
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last updated on 2024-10-09 20:33 CEST by the dblp team
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