Soledad Villar

72 papers A* 8A 1B 1Misc 3Journal 54Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Learning Rate Scaling across LoRA Ranks and Transfer to Full Finetuning. CoRR Nan Chen, Soledad Villar, Soufiane Hayou
2026	J	`jnl`	μpscaling small models: Principled warm starts and hyperparameter transfer. CoRR Yuxin Ma, Nan Chen, Mateo Díaz, Soufiane Hayou, Dmitriy Kunisky, Soledad Villar
2025	J	`jnl`	Empirical Power Analysis of a Statistical Test to Quantify Gerrymandering. CoRR Ranthony A. Clark, Susan Glenn, Harlin Lee, Soledad Villar
2025	J	`jnl`	Functions on Symmetric Matrices and Point Clouds via Lightweight Invariant Features from Galois Theory. SIAM J. Appl. Algebra Geom. Ben Blum-Smith, Ningyuan Teresa Huang, Marco Cuturi, Soledad Villar
2025	J	`jnl`	Group Averaging for Physics Applications: Accuracy Improvements at Zero Training Cost. CoRR Valentino F. Foit, David W. Hogg, Soledad Villar
2025	J	`jnl`	Multi-View Graph Learning with Graph-Tuple. CoRR Shiyu Chen, Ningyuan Huang, Soledad Villar
2025	J	`jnl`	On Transferring Transferability: Towards a Theory for Size Generalization. CoRR Eitan Levin, Yuxin Ma, Mateo Díaz, Soledad Villar
2025	J	`jnl`	The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS). CoRR Andrew Ferguson, Marisa Lafleur, Lars Ruthotto, Jesse Thaler, Yuan-Sen Ting, Pratyush Tiwary, Soledad Villar, E. Paulo Alves, Jeremy Avigad, Simon Billinge, Camille L. Bilodeau, Keith Brown, Emmanuel J. Candès, Arghya Chattopadhyay, Bingqing Cheng, Jonathan Clausen, Connor W. Coley, Andrew J. Connolly, Fred Daum, Sijia S. Dong, Chrisy Xiyu Du, Cora Dvorkin, Cristiano Fanelli, Eric B. Ford, Luis Manuel Frutos, Nicolás García Trillos, Cecilia Garraffo, Robert Ghrist, Rafael Gómez-Bombarelli, Gianluca Guadagni, Sreelekha Guggilam, Sergei Gukov, Juan B. Gutierrez, Salman Habib, Johannes Hachmann, Boris Hanin, Philip C. Harris, Murray Holland, Elizabeth Holm, Hsin-Yuan Huang, Shih-Chieh Hsu, Nick Jackson, Olexandr Isayev, Heng Ji, Aggelos K. Katsaggelos, Jeremy Kepner, Yannis G. Kevrekidis, Michelle P. Kuchera, J. Nathan Kutz, Branislava Lalic, Ann Lee, Matt LeBlanc, Josiah Lim, Rebecca Lindsey, Yongmin Liu, Peter Y. Lu, Sudhir Malik, Vuk Mandic, Vidya B. Manian, Emeka P. Mazi, Pankaj Mehta, Peter Melchior, Brice Ménard, Jennifer Ngadiuba, Stella Offner, Elsa Olivetti, Shyue Ping Ong, Christopher Rackauckas, Philippe Rigollet, Chad Risko, Philip Romero, Grant M. Rotskoff, Brett Savoie, Uros Seljak, David Shih, Gary Shiu, Dima Shlyakhtenko, Eva Silverstein, Taylor Sparks, Thomas Strohmer, Christopher Stubbs, Stephen Thomas, Suriyanarayanan Vaikuntanathan, René Vidal, Francisco Villaescusa-Navarro, Gregory Voth, Benjamin Wandelt, Rachel Ward, Melanie Weber, Risa Wechsler, Stephen Whitelam, Olaf Wiest, Mike Williams, Zhuoran Yang, Yaroslava G. Yingling, Bin Yu, Shuwen Yue, Ann Zabludoff, Huimin Zhao, Tong Zhang
2025	J	`jnl`	Towards Coordinate- and Dimension-Agnostic Machine Learning for Partial Differential Equations. CoRR Trung V. Phan, George A. Kevrekidis, Soledad Villar, Yannis G. Kevrekidis, Juan M. Bello-Rivas
2025	J	`jnl`	ginjax: E(d)-Equivariant CNN for Tensor Images. J. Open Source Softw. Wilson Gregory, Kaze W. K. Wong, David W. Hogg, Soledad Villar
2024	Misc	`conf`	A Spectral Analysis of Graph Neural Networks on Dense and Sparse Graphs. ICASSP Luana Ruiz, Ningyuan Teresa Huang, Soledad Villar
2024	A*	`conf`	Graph neural networks and non-commuting operators. NeurIPS Mauricio Velasco, Kaiying O'Hare, Bernardo Rychtenberg, Soledad Villar
2024	J	`jnl`	Graph neural networks and non-commuting operators. CoRR Mauricio Velasco, Kaiying O'Hare, Bernardo Rychtenberg, Soledad Villar
2024	J	`jnl`	Is machine learning good or bad for the natural sciences? CoRR David W. Hogg, Soledad Villar
2024	J	`jnl`	Learning equivariant tensor functions with applications to sparse vector recovery. CoRR Wilson Gregory, Josué Tonelli-Cueto, Nicholas F. Marshall, Andrew S. Lee, Soledad Villar
2024	J	`jnl`	Learning functions on symmetric matrices and point clouds via lightweight invariant features. CoRR Ben Blum-Smith, Ningyuan Huang, Marco Cuturi, Soledad Villar
2024	A*	`conf`	Position: Is machine learning good or bad for the natural sciences? ICML David W. Hogg, Soledad Villar
2024	A*	`conf`	Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning. ICLR Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka
2024	J	`jnl`	Thinner Latent Spaces: Detecting dimension and imposing invariance through autoencoder gradient constraints. CoRR George A. Kevrekidis, Mauro Maggioni, Soledad Villar, Yannis G. Kevrekidis
2024	J	`jnl`	Towards fully covariant machine learning. Trans. Mach. Learn. Res. Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf
2024	—	`conf`	Universal Transferability for GNNs Via Graphons. IEEECONF Mauricio Velasco, Kaiying O'Hare, Bernardo Rychtenberg, Soledad Villar
2023	A*	`conf`	Approximately Equivariant Graph Networks. NeurIPS Ningyuan Huang, Ron Levie, Soledad Villar
2023	J	`jnl`	Approximately Equivariant Graph Networks. CoRR Ningyuan Huang, Ron Levie, Soledad Villar
2023	J	`jnl`	Dimensionless machine learning: Imposing exact units equivariance. J. Mach. Learn. Res. Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu
2023	A*	`conf`	Fine-grained Expressivity of Graph Neural Networks. NeurIPS Jan Böker, Ron Levie, Ningyuan Huang, Soledad Villar, Christopher Morris
2023	J	`jnl`	Fine-grained Expressivity of Graph Neural Networks. CoRR Jan Böker, Ron Levie, Ningyuan Huang, Soledad Villar, Christopher Morris
2023	J	`jnl`	GeometricImageNet: Extending convolutional neural networks to vector and tensor images. CoRR Wilson Gregory, David W. Hogg, Ben Blum-Smith, Maria Teresa Arias, Kaze W. K. Wong, Soledad Villar
2023	—	`ed.`	Learning on Graphs Conference, 27-30 November 2023, Virtual Event. LoG Soledad Villar, Benjamin Chamberlain
2023	—	`conf`	Prospective Learning: Principled Extrapolation to the Future. CoLLAs 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 S. 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
2023	J	`jnl`	Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning. CoRR Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka
2023	—	`conf`	The Second Learning on Graphs Conference: Preface. LoG 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
2023	J	`jnl`	The passive symmetries of machine learning. CoRR Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf
2022	J	`jnl`	A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants. CoRR Ningyuan Huang, Soledad Villar
2022	J	`jnl`	Deep Learning is Provably Robust to Symmetric Label Noise. CoRR Carey E. Priebe, Ningyuan Huang, Soledad Villar, Cong Mu, Li Chen
2022	J	`jnl`	Dimensionality Reduction, Regularization, and Generalization in Overparameterized Regressions. SIAM J. Math. Data Sci. Ningyuan Teresa Huang, David W. Hogg, Soledad Villar
2022	J	`jnl`	Dimensionless machine learning: Imposing exact units equivariance. CoRR Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu
2022	J	`jnl`	Equivariant maps from invariant functions. CoRR Ben Blum-Smith, Soledad Villar
2022	J	`jnl`	From Local to Global: Spectral-Inspired Graph Neural Networks. CoRR Ningyuan Huang, Soledad Villar, Carey E. Priebe, Da Zheng, Chengyue Huang, Lin Yang, Vladimir Braverman
2022	J	`jnl`	Graph Neural Networks for Community Detection on Sparse Graphs. CoRR Luana Ruiz, Ningyuan Huang, Soledad Villar
2022	J	`jnl`	MarkerMap: nonlinear marker selection for single-cell studies. CoRR Nabeel Sarwar, Wilson Gregory, George A. Kevrekidis, Soledad Villar, Bianca Dumitrascu
2022	J	`jnl`	Shuffled linear regression through graduated convex relaxation. CoRR Efe Onaran, Soledad Villar
2022	J	`jnl`	Sketch-and-solve approaches to k-means clustering by semidefinite programming. CoRR Charles Clum, Dustin G. Mixon, Soledad Villar, Kaiying Xie
2021	Misc	`conf`	A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants. ICASSP Ningyuan Teresa Huang, Soledad Villar
2021	J	`jnl`	A simple equivariant machine learning method for dynamics based on scalars. CoRR Weichi Yao, Kate Storey-Fisher, David W. Hogg, Soledad Villar
2021	J	`jnl`	Fitting very flexible models: Linear regression with large numbers of parameters. CoRR David W. Hogg, Soledad Villar
2021	A*	`conf`	Scalars are universal: Equivariant machine learning, structured like classical physics. NeurIPS Soledad Villar, David W. Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith
2021	J	`jnl`	Scalars are universal: Gauge-equivariant machine learning, structured like classical physics. CoRR Soledad Villar, David W. Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith
2020	A*	`conf`	Can Graph Neural Networks Count Substructures? NeurIPS Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna
2020	J	`jnl`	Can graph neural networks count substructures? CoRR Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna
2020	J	`jnl`	Dimensionality reduction, regularization, and generalization in overparameterized regressions. CoRR Ningyuan Huang, David W. Hogg, Soledad Villar
2020	Misc	`conf`	Efficient Belief Propagation for Graph Matching. ICASSP Efe Onaran, Soledad Villar
2020	J	`jnl`	MREC: a fast and versatile framework for aligning and matching point clouds with applications to single cell molecular data. CoRR Andrew J. Blumberg, Mathieu Carrière, Michael A. Mandell, Raul Rabadan, Soledad Villar
2020	J	`jnl`	SqueezeFit: Label-Aware Dimensionality Reduction by Semidefinite Programming. IEEE Trans. Inf. Theory Culver McWhirter, Dustin G. Mixon, Soledad Villar
2019	J	`jnl`	Experimental performance of graph neural networks on random instances of max-cut. CoRR Weichi Yao, Afonso S. Bandeira, Soledad Villar
2019	J	`jnl`	Fair redistricting is hard. Theor. Comput. Sci. Richard Kueng, Dustin G. Mixon, Soledad Villar
2019	A*	`conf`	On the equivalence between graph isomorphism testing and function approximation with GNNs. NeurIPS Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna
2019	J	`jnl`	On the equivalence between graph isomorphism testing and function approximation with GNNs. CoRR Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna
2018	J	`jnl`	Fair redistricting is hard. CoRR Richard Kueng, Dustin G. Mixon, Soledad Villar
2018	—	`conf`	Revised Note on Learning Quadratic Assignment with Graph Neural Networks. DSW Alex Nowak, Soledad Villar, Afonso S. Bandeira, Joan Bruna
2018	J	`jnl`	SUNLayer: Stable denoising with generative networks. CoRR Dustin G. Mixon, Soledad Villar
2018	J	`jnl`	SqueezeFit: Label-aware dimensionality reduction by semidefinite programming. CoRR Culver McWhirter, Dustin G. Mixon, Soledad Villar
2018	J	`jnl`	Utility Ghost: Gamified redistricting with partisan symmetry. CoRR Dustin G. Mixon, Soledad Villar
2017	J	`jnl`	A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks. CoRR Alex Nowak, Soledad Villar, Afonso S. Bandeira, Joan Bruna
2017	J	`jnl`	Probably certifiably correct k-means clustering. Math. Program. Takayuki Iguchi, Dustin G. Mixon, Jesse Peterson, Soledad Villar
2017	J	`jnl`	Projected Power Iteration for Network Alignment. CoRR Efe Onaran, Soledad Villar
2016	J	`jnl`	A polynomial-time relaxation of the Gromov-Hausdorff distance. CoRR Soledad Villar, Afonso S. Bandeira, Andrew J. Blumberg, Rachel A. Ward
2016	J	`jnl`	Clustering subgaussian mixtures by semidefinite programming. CoRR Dustin G. Mixon, Soledad Villar, Rachel A. Ward
2016	B	`conf`	Clustering subgaussian mixtures with k-means. ITW Dustin G. Mixon, Soledad Villar, Rachel A. Ward
2015	J	`jnl`	On the tightness of an SDP relaxation of k-means. CoRR Takayuki Iguchi, Dustin G. Mixon, Jesse Peterson, Soledad Villar
2015	J	`jnl`	Probably certifiably correct k-means clustering. CoRR Takayuki Iguchi, Dustin G. Mixon, Jesse Peterson, Soledad Villar
2015	A	`conf`	Relax, No Need to Round: Integrality of Clustering Formulations. ITCS Pranjal Awasthi, Afonso S. Bandeira, Moses Charikar, Ravishankar Krishnaswamy, Soledad Villar, Rachel A. Ward
2014	J	`jnl`	Relax, no need to round: integrality of clustering formulations. CoRR Pranjal Awasthi, Afonso S. Bandeira, Moses Charikar, Ravishankar Krishnaswamy, Soledad Villar, Rachel A. Ward