:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Khurana, Varun, Cheng, Xiuyuan, Cloninger, Alexander
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	https://arxiv.org/abs/2407.04806
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Kernel Two-Sample Tests for Manifold Data
por: Cheng, Xiuyuan, et al.
Publicado: (2021)

Linearized Optimal Transport pyLOT Library: A Toolkit for Machine Learning on Point Clouds
por: Linwu, Jun, et al.
Publicado: (2025)

Point Cloud Classification via Deep Set Linearized Optimal Transport
por: Mahan, Scott, et al.
Publicado: (2024)

Wasserstein approximation schemes based on Voronoi partitions
por: Hamm, Keaton, et al.
Publicado: (2023)

Revisiting Meta-Learning with Noisy Labels: Reweighting Dynamics and Theoretical Guarantees
por: Zhang, Yiming, et al.
Publicado: (2025)

Kernel Two-Sample Testing via Directional Components Analysis
por: Cui, Rui, et al.
Publicado: (2025)

Variable Selection for Kernel Two-Sample Tests
por: Wang, Jie, et al.
Publicado: (2023)

Boosting the Power of Kernel Two-Sample Tests
por: Chatterjee, Anirban, et al.
Publicado: (2023)

A Semi-Supervised Kernel Two-Sample Test
por: Lee, Gyumin, et al.
Publicado: (2026)

A Permutation-free Kernel Two-Sample Test
por: Shekhar, Shubhanshu, et al.
Publicado: (2022)

Does Sparse Connectivity Improve Generalization? Convolutional Networks Below the Edge of Stability
por: Liang, Tongtong, et al.
Publicado: (2026)

Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective
por: Takakura, Shokichi, et al.
Publicado: (2024)

The Neural Tangent Kernel for Classification
por: Plenk, Jonathan, et al.
Publicado: (2026)

Semi-Supervised Laplace Learning on Stiefel Manifolds
por: Holtz, Chester, et al.
Publicado: (2023)

A Scalable Nystrom-Based Kernel Two-Sample Test with Permutations
por: Chatalic, Antoine, et al.
Publicado: (2025)

Neural network-based CUSUM for online change-point detection
por: Gong, Tingnan, et al.
Publicado: (2022)

Flow-based generative models as iterative algorithms in probability space
por: Xie, Yao, et al.
Publicado: (2025)

Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation
por: Cheng, Xiuyuan, et al.
Publicado: (2021)

Learning manifold diffusion semigroups from graph transition matrices
por: Cheng, Xiuyuan, et al.
Publicado: (2026)

Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise
por: Cheng, Xiuyuan, et al.
Publicado: (2022)

Spectral Analysis of Molecular Kernels: When Richer Features Do Not Guarantee Better Generalization
por: Jamali, Asma, et al.
Publicado: (2025)

Kernel Two-Sample Tests in High Dimension: Interplay Between Moment Discrepancy and Dimension-and-Sample Orders
por: Yan, Jian, et al.
Publicado: (2021)

Generalization Below the Edge of Stability: The Role of Data Geometry
por: Liang, Tongtong, et al.
Publicado: (2025)

Linearized Optimal Transport for Analysis of High-Dimensional Point-Cloud and Single-Cell Data
por: Wang, Tianxiang, et al.
Publicado: (2025)

Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features
por: Choi, Ikjun, et al.
Publicado: (2024)

Kernel Selection is Model Selection: A Unified Complexity-Penalized Approach for MMD Two-Sample Tests
por: Ni, Yijin, et al.
Publicado: (2026)

On the Sample Complexity of Robust Binary Hypothesis Testing
por: Vallinayagam, Shankar, et al.
Publicado: (2026)

SGM-PINN: Sampling Graphical Models for Faster Training of Physics-Informed Neural Networks
por: Anticev, John, et al.
Publicado: (2024)

FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee
por: Li, Puheng, et al.
Publicado: (2022)

GeoEdit: Local Frames for Fast, Training-Free On-Manifold Editing in Diffusion Models
por: Zhang, Yiming, et al.
Publicado: (2026)

Local Flow Matching Generative Models
por: Xu, Chen, et al.
Publicado: (2024)

Normalizing flow neural networks by JKO scheme
por: Xu, Chen, et al.
Publicado: (2022)

An alternative approach to train neural networks using monotone variational inequality
por: Xu, Chen, et al.
Publicado: (2022)

Generative models for decision-making under distributional shift
por: Cheng, Xiuyuan, et al.
Publicado: (2026)

G-invariant diffusion maps
por: Rosen, Eitan, et al.
Publicado: (2023)

Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee
por: Chen, George H.
Publicado: (2022)

Robust Tangent Space Estimation via Laplacian Eigenvector Gradient Orthogonalization
por: Kohli, Dhruv, et al.
Publicado: (2025)

Distance and Kernel-Based Measures for Global and Local Two-Sample Conditional Distribution Testing
por: Yan, Jian, et al.
Publicado: (2022)

Graph Kernel Neural Networks
por: Cosmo, Luca, et al.
Publicado: (2021)

The Sample Complexity of Simple Binary Hypothesis Testing
por: Pensia, Ankit, et al.
Publicado: (2024)