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ORCIDNicolás García Trillos
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2020 – today
- 2024
[j17]Nicolás García Trillos, Anna V. Little, Daniel McKenzie, James M. Murphy:
Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms. J. Mach. Learn. Res. 25: 176:1-176:65 (2024)- [j16]José A. Carrillo, Nicolás García Trillos, Sixu Li, Yuhua Zhu:
FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization. J. Mach. Learn. Res. 25: 214:1-214:51 (2024) - [i31]Nicolás García Trillos, Matt Jacobs, Jakwang Kim, Matthew Werenski:
An Optimal Transport Approach for Computing Adversarial Training Lower Bounds in Multiclass Classification. CoRR abs/2401.09191 (2024) - 2023
- [j15]Nicolás García Trillos, Matt Jacobs, Jakwang Kim:
The multimarginal optimal transport formulation of adversarial multiclass classification. J. Mach. Learn. Res. 24: 45:1-45:56 (2023) - [j14]Nicolás García Trillos, Pengfei He, Chenghui Li:
Large sample spectral analysis of graph-based multi-manifold clustering. J. Mach. Learn. Res. 24: 143:1-143:71 (2023) - [i30]Camilo Garcia Trillos, Nicolás García Trillos:
On adversarial robustness and the use of Wasserstein ascent-descent dynamics to enforce it. CoRR abs/2301.03662 (2023) - [i29]Nicolás García Trillos, Matt Jacobs, Jakwang Kim:
On the existence of solutions to adversarial training in multiclass classification. CoRR abs/2305.00075 (2023) - [i28]José A. Carrillo, Nicolás García Trillos, Sixu Li, Yuhua Zhu:
FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization. CoRR abs/2305.02894 (2023) - [i27]Leon Bungert, Nicolás García Trillos, Matt Jacobs, Daniel McKenzie, Dorde Nikolic, Qingsong Wang:
It begins with a boundary: A geometric view on probabilistically robust learning. CoRR abs/2305.18779 (2023) - [i26]Nicolás García Trillos, Melanie Weber:
Continuum Limits of Ollivier's Ricci Curvature on data clouds: pointwise consistency and global lower bounds. CoRR abs/2307.02378 (2023) - [i25]Nicolás García Trillos, Anna V. Little, Daniel McKenzie, James M. Murphy:
Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms. CoRR abs/2307.05750 (2023) - [i24]Chenghui Li, Rishi Sonthalia, Nicolás García Trillos:
Spectral Neural Networks: Approximation Theory and Optimization Landscape. CoRR abs/2310.00729 (2023) - [i23]Nicolás García Trillos, Bodhisattva Sen:
A New Perspective On Denoising Based On Optimal Transport. CoRR abs/2312.08135 (2023) - 2022
- [j13]Nicolás García Trillos, Ryan Murray:
Adversarial Classification: Necessary Conditions and Geometric Flows. J. Mach. Learn. Res. 23: 187:1-187:38 (2022) - [j12]Nicolás García Trillos, Javier Morales:
Semi-discrete Optimization Through Semi-discrete Optimal Transport: A Framework for Neural Architecture Search. J. Nonlinear Sci. 32(3): 27 (2022) - [j11]Jeff Calder
, Nicolás García Trillos, Marta Lewicka:
Lipschitz Regularity of Graph Laplacians on Random Data Clouds. SIAM J. Math. Anal. 54(1): 1169-1222 (2022) - [i22]Nicolás García Trillos, Matt Jacobs, Jakwang Kim:
The Multimarginal Optimal Transport Formulation of Adversarial Multiclass Classification. CoRR abs/2204.12676 (2022) - [i21]Nicolás García Trillos, Daniel Sanz-Alonso, Ruiyi Yang:
Mathematical Foundations of Graph-Based Bayesian Semi-Supervised Learning. CoRR abs/2207.01093 (2022) - [i20]Nicolás García Trillos, Ryan Murray, Matthew Thorpe:
Rates of Convergence for Regression with the Graph Poly-Laplacian. CoRR abs/2209.02305 (2022) - [i19]Yuetian Luo, Nicolás García Trillos:
Nonconvex Matrix Factorization is Geodesically Convex: Global Landscape Analysis for Fixed-rank Matrix Optimization From a Riemannian Perspective. CoRR abs/2209.15130 (2022) - [i18]Aditya Kumar Akash, Sixu Li, Nicolás García Trillos:
Wasserstein Barycenter-based Model Fusion and Linear Mode Connectivity of Neural Networks. CoRR abs/2210.06671 (2022) - 2021
- [j10]Nicolás García Trillos, Franca Hoffmann, Bamdad Hosseini:
Geometric structure of graph Laplacian embeddings. J. Mach. Learn. Res. 22: 63:1-63:55 (2021) - [c1]Nicolás García Trillos, Félix Morales, Javier Morales:
Traditional and Accelerated Gradient Descent for Neural Architecture Search. GSI 2021: 507-514 - [i17]Nicolás García Trillos, Pengfei He, Chenghui Li:
Large sample spectral analysis of graph-based multi-manifold clustering. CoRR abs/2107.13610 (2021) - [i16]Katy Craig, Nicolás García Trillos, Dejan Slepcev:
Clustering dynamics on graphs: from spectral clustering to mean shift through Fokker-Planck interpolation. CoRR abs/2108.08687 (2021) - [i15]Camilo Garcia Trillos, Nicolás García Trillos:
On the regularized risk of distributionally robust learning over deep neural networks. CoRR abs/2109.06294 (2021) - [i14]Leon Bungert, Nicolás García Trillos, Ryan Murray:
The Geometry of Adversarial Training in Binary Classification. CoRR abs/2111.13613 (2021) - 2020
- [j9]Nicolás García Trillos, Moritz Gerlach, Matthias Hein, Dejan Slepcev:
Error Estimates for Spectral Convergence of the Graph Laplacian on Random Geometric Graphs Toward the Laplace-Beltrami Operator. Found. Comput. Math. 20(4): 827-887 (2020) - [j8]Nicolás García Trillos, Zachary Kaplan, Thabo Samakhoana, Daniel Sanz-Alonso:
On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms. J. Mach. Learn. Res. 21: 28:1-28:47 (2020) - [j7]Nicolás García Trillos, Ryan W. Murray:
A Maximum Principle Argument for the Uniform Convergence of Graph Laplacian Regressors. SIAM J. Math. Data Sci. 2(3): 705-739 (2020) - [i13]Daniele Bigoni, Yuming Chen, Nicolás García Trillos, Youssef M. Marzouk, Daniel Sanz-Alonso:
Data-Driven Forward Discretizations for Bayesian Inversion. CoRR abs/2003.07991 (2020) - [i12]Nicolás García Trillos, Ryan Murray, Matthew Thorpe:
From graph cuts to isoperimetric inequalities: Convergence rates of Cheeger cuts on data clouds. CoRR abs/2004.09304 (2020) - [i11]Nicolás García Trillos, Félix Morales, Javier Morales:
Traditional and accelerated gradient descent for neural architecture search. CoRR abs/2006.15218 (2020) - [i10]Nicolás García Trillos, Javier Morales:
Semi-discrete optimization through semi-discrete optimal transport: a framework for neural architecture search. CoRR abs/2006.15221 (2020) - [i9]Jeff Calder, Nicolás García Trillos, Marta Lewicka:
Lipschitz regularity of graph Laplacians on random data clouds. CoRR abs/2007.06679 (2020) - [i8]Nicolás García Trillos, Ryan Murray:
Adversarial Classification: Necessary conditions and geometric flows. CoRR abs/2011.10797 (2020)
2010 – 2019
- 2019
- [j6]Nicolás García Trillos, Zachary Kaplan, Daniel Sanz-Alonso:
Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning. Entropy 21(5): 511 (2019) - [j5]Nicolás García Trillos, Daniel Sanz-Alonso, Ruiyi Yang:
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis. J. Mach. Learn. Res. 20: 136:1-136:37 (2019) - [j4]Nicolás García Trillos:
Variational Limits of k-NN Graph-Based Functionals on Data Clouds. SIAM J. Math. Data Sci. 1(1): 93-120 (2019) - [i7]Nicolás García Trillos, Zachary Kaplan, Daniel Sanz-Alonso:
Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning. CoRR abs/1901.10082 (2019) - [i6]Nicolás García Trillos, Ryan Murray:
A maximum principle argument for the uniform convergence of graph Laplacian regressors. CoRR abs/1901.10089 (2019) - [i5]Nicolás García Trillos, Daniel Sanz-Alonso, Ruiyi Yang:
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis. CoRR abs/1904.03335 (2019) - [i4]Jeff Calder, Nicolás García Trillos:
Improved spectral convergence rates for graph Laplacians on epsilon-graphs and k-NN graphs. CoRR abs/1910.13476 (2019) - 2018
- [j3]Nicolás García Trillos, Daniel Sanz-Alonso:
Continuum Limits of Posteriors in Graph Bayesian Inverse Problems. SIAM J. Math. Anal. 50(4): 4020-4040 (2018) - 2017
- [j2]Aaditya Ramdas, Nicolás García Trillos, Marco Cuturi:
On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests. Entropy 19(2): 47 (2017) - [i3]Nicolás García Trillos, Zachary Kaplan, Thabo Samakhoana, Daniel Sanz-Alonso:
On the Consistency of Graph-based Bayesian Learning and the Scalability of Sampling Algorithms. CoRR abs/1710.07702 (2017) - 2016
- [j1]Nicolás García Trillos, Dejan Slepcev, James H. von Brecht, Thomas Laurent, Xavier Bresson:
Consistency of Cheeger and Ratio Graph Cuts. J. Mach. Learn. Res. 17: 181:1-181:46 (2016) - 2015
- [i2]Nicolás García Trillos, Dejan Slepcev:
A variational approach to the consistency of spectral clustering. CoRR abs/1508.01928 (2015) - 2014
- [i1]Nicolás García Trillos, Dejan Slepcev, James H. von Brecht, Thomas Laurent, Xavier Bresson:
Consistency of Cheeger and Ratio Graph Cuts. CoRR abs/1411.6590 (2014)
Coauthor Index
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