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Publication search results
found 47 matches
- 2024
- 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
- 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) - 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) - 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) - Nicolás García Trillos, Matt Jacobs, Jakwang Kim:
On the existence of solutions to adversarial training in multiclass classification. CoRR abs/2305.00075 (2023) - Jose 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) - 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) - 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) - Nicolás García Trillos, Anna Little, Daniel McKenzie, James M. Murphy:
Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms. CoRR abs/2307.05750 (2023) - Chenghui Li, Rishi Sonthalia, Nicolás García Trillos:
Spectral Neural Networks: Approximation Theory and Optimization Landscape. CoRR abs/2310.00729 (2023) - Nicolás García Trillos, Bodhisattva Sen:
A New Perspective On Denoising Based On Optimal Transport. CoRR abs/2312.08135 (2023) - 2022
- Nicolás García Trillos, Ryan Murray:
Adversarial Classification: Necessary Conditions and Geometric Flows. J. Mach. Learn. Res. 23: 187:1-187:38 (2022) - 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) - 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) - Nicolás García Trillos, Matt Jacobs, Jakwang Kim:
The Multimarginal Optimal Transport Formulation of Adversarial Multiclass Classification. CoRR abs/2204.12676 (2022) - Nicolás García Trillos, Daniel Sanz-Alonso, Ruiyi Yang:
Mathematical Foundations of Graph-Based Bayesian Semi-Supervised Learning. CoRR abs/2207.01093 (2022) - Nicolás García Trillos, Ryan Murray, Matthew Thorpe:
Rates of Convergence for Regression with the Graph Poly-Laplacian. CoRR abs/2209.02305 (2022) - 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) - 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
- Nicolás García Trillos, Franca Hoffmann, Bamdad Hosseini:
Geometric structure of graph Laplacian embeddings. J. Mach. Learn. Res. 22: 63:1-63:55 (2021) - Nicolás García Trillos, Félix Morales, Javier Morales:
Traditional and Accelerated Gradient Descent for Neural Architecture Search. GSI 2021: 507-514 - Nicolás García Trillos, Pengfei He, Chenghui Li:
Large sample spectral analysis of graph-based multi-manifold clustering. CoRR abs/2107.13610 (2021) - 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) - Camilo Garcia Trillos, Nicolás García Trillos:
On the regularized risk of distributionally robust learning over deep neural networks. CoRR abs/2109.06294 (2021) - Leon Bungert, Nicolás García Trillos, Ryan Murray:
The Geometry of Adversarial Training in Binary Classification. CoRR abs/2111.13613 (2021) - 2020
- 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) - 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) - 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) - 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) - 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)
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