Nicolás García Trillos
Nicolás García Trillos
Assistant Professor, Department of Statistics, University of Wisconsin-Madison
Verified email at wisc.edu - Homepage
Title
Cited by
Cited by
Year
On wasserstein two-sample testing and related families of nonparametric tests
A Ramdas, NG Trillos, M Cuturi
Entropy 19 (2), 47, 2017
1952017
Continuum limit of total variation on point clouds
NG Trillos, D Slepčev
Archive for rational mechanics and analysis 220 (1), 193-241, 2016
962016
A variational approach to the consistency of spectral clustering
NG Trillos, D Slepčev
Applied and Computational Harmonic Analysis 45 (2), 239-281, 2018
952018
Error estimates for spectral convergence of the graph Laplacian on random geometric graphs toward the Laplace–Beltrami operator
NG Trillos, M Gerlach, M Hein, D Slepčev
Foundations of Computational Mathematics 20 (4), 827-887, 2020
912020
On the rate of convergence of empirical measures in∞-transportation distance
NG Trillos, D Slepčev
Canadian Journal of Mathematics 67 (6), 1358-1383, 2015
752015
Consistency of Cheeger and ratio graph cuts
NG Trillos, D Slepčev, J Von Brecht, T Laurent, X Bresson
The Journal of Machine Learning Research 17 (1), 6268-6313, 2016
742016
Improved spectral convergence rates for graph Laplacians on epsilon-graphs and k-NN graphs
J Calder, NG Trillos
arXiv preprint arXiv:1910.13476, 2019
262019
Continuum limits of posteriors in graph Bayesian inverse problems
N García Trillos, D Sanz-Alonso
SIAM Journal on Mathematical Analysis 50 (4), 4020-4040, 2018
242018
The Bayesian formulation and well-posedness of fractional elliptic inverse problems
NG Trillos, D Sanz-Alonso
Inverse Problems 33 (6), 065006, 2017
192017
A new analytical approach to consistency and overfitting in regularized empirical risk minimization
NG Trillos, R Murray
European Journal of Applied Mathematics 28 (6), 886-921, 2017
182017
Gromov–Hausdorff limit of Wasserstein spaces on point clouds
NG Trillos
Calculus of Variations and Partial Differential Equations 59 (2), 1-43, 2020
132020
Geometric structure of graph Laplacian embeddings
NG Trillos, F Hoffmann, B Hosseini
Journal of Machine Learning Research 22 (63), 1-55, 2021
122021
On the consistency of graph-based Bayesian learning and the scalability of sampling algorithms
NG Trillos, Z Kaplan, T Samakhoana, D Sanz-Alonso
arXiv preprint arXiv:1710.07702, 2017
122017
Variational Limits of -NN Graph-Based Functionals on Data Clouds
N Garcia Trillos
SIAM Journal on Mathematics of Data Science 1 (1), 93-120, 2019
112019
The Bayesian update: variational formulations and gradient flows
NG Trillos, D Sanz-Alonso
Bayesian Analysis 15 (1), 29-56, 2020
102020
A maximum principle argument for the uniform convergence of graph Laplacian regressors
N García Trillos, RW Murray
SIAM Journal on Mathematics of Data Science 2 (3), 705-739, 2020
102020
On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms.
NG Trillos, Z Kaplan, T Samakhoana, D Sanz-Alonso
J. Mach. Learn. Res. 21, 28:1-28:47, 2020
82020
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis.
NG Trillos, D Sanz-Alonso, R Yang
J. Mach. Learn. Res. 20, 136:1-136:37, 2019
82019
Estimating perimeter using graph cuts
NG Trillos, D Slepčev, J Von Brecht
Advances in Applied Probability 49 (4), 1067-1090, 2017
82017
Spectral convergence of empirical graph Laplacians
NG Trillos, M Gerlach, M Hein, D Slepcev
preparation, 2017
72017
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