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Geodesic Exponential Kernels: When Curvature and Linearity Conflict

2 November 2014

Papers citing "Geodesic Exponential Kernels: When Curvature and Linearity Conflict"

50 / 72 papers shown

Title
Categorical and geometric methods in statistical, manifold, and machine learning H. Lê Hà Quang Minh Frederic Protin W. Tuschmann AI4CE 57 0 0 06 May 2025
A probabilistic view on Riemannian machine learning models for SPD matrices Thibault de Surrel Florian Yger Fabien Lotte Sylvain Chevallier 53 0 0 05 May 2025
Scalable Geometric Learning with Correlation-Based Functional Brain Networks Kisung You Yelim Lee Hae-Jeong Park 99 0 0 31 Mar 2025
Learning from Summarized Data: Gaussian Process Regression with Sample Quasi-Likelihood Yuta Shikuri GP 111 0 0 23 Dec 2024
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation Koshi Watanabe Keisuke Maeda Takahiro Ogawa Miki Haseyama 404 0 0 22 Oct 2024
Bayesian Optimal Experimental Design for Robot Kinematic Calibration Ersin Daş Thomas Touma J. W. Burdick 123 0 0 17 Sep 2024
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs P. Mostowsky Vincent Dutordoir I. Azangulov Noémie Jaquier Michael John Hutchinson Aditya Ravuri Leonel Rozo Alexander Terenin Viacheslav Borovitskiy 76 6 0 10 Jul 2024
A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences Miguel González Duque Richard Michael Simon Bartels Yevgen Zainchkovskyy Søren Hauberg Wouter Boomsma 68 5 0 07 Jun 2024
Scalable Bayesian inference for heat kernel Gaussian processes on manifolds Junhui He Guoxuan Ma Jian Kang Ying Yang 50 0 0 22 May 2024
Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates Rémi Leluc Aymeric Dieuleveut Franccois Portier Johan Segers Aigerim Zhuman 78 7 0 02 Feb 2024
Metric Space Magnitude for Evaluating the Diversity of Latent Representations K. Limbeck R. Andreeva Rik Sarkar Bastian Rieck 180 4 0 27 Nov 2023
Implicit Manifold Gaussian Process Regression Bernardo Fichera Viacheslav Borovitskiy Andreas Krause A. Billard 50 4 0 30 Oct 2023
Invariant kernels on Riemannian symmetric spaces: a harmonic-analytic approach Nathael Da Costa Cyrus Mostajeran Juan-Pablo Ortega Salem Said 59 4 0 30 Oct 2023
Geometric Learning with Positively Decomposable Kernels Nathael Da Costa Cyrus Mostajeran Juan-Pablo Ortega Salem Said 52 3 0 20 Oct 2023
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature Space Kaushik Roy Peyman Moghadam Mehrtash Harandi 91 7 0 31 Jul 2023
Stability and Inference of the Euler Characteristic Transform L. Marsh David Beers 88 6 0 23 Mar 2023
Gaussian Process on the Product of Directional Manifolds Ziyu Cao Kailai Li GP 169 1 0 13 Mar 2023
Gaussian kernels on non-simply-connected closed Riemannian manifolds are never positive definite Siran Li 53 3 0 12 Mar 2023
The Gaussian kernel on the circle and spaces that admit isometric embeddings of the circle Nathael Da Costa Cyrus Mostajeran Juan-Pablo Ortega BDL 59 4 0 21 Feb 2023
Horospherical Decision Boundaries for Large Margin Classification in Hyperbolic Space Xiran Fan Chun-Hao Yang B. Vemuri 58 8 0 14 Feb 2023
Curvature Filtrations for Graph Generative Model Evaluation Joshua Southern Jeremy Wayland Michael M. Bronstein Bastian Rieck 82 17 0 30 Jan 2023
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics Mu Niu Zhenwen Dai P. Cheung Yizhu Wang 63 5 0 16 Jan 2023
Gaussian Process regression over discrete probability measures: on the non-stationarity relation between Euclidean and Wasserstein Squared Exponential Kernels Antonio Candelieri Andrea Ponti Francesco Archetti 88 1 0 02 Dec 2022
Hyperbolic Sliced-Wasserstein via Geodesic and Horospherical Projections Clément Bonet Laetitia Chapel Lucas Drumetz Nicolas Courty 80 15 0 18 Nov 2022
Hyperbolic Centroid Calculations for Text Classification Aydın Gerek Cuneyt Ferahlar Bilge cSipal Sert Mehmet Can Yuney Onur Tacsdemir Zeynep Billur Kalafat Mert Kelkit M. Ganiz 46 0 0 08 Nov 2022
Optimization on Manifolds via Graph Gaussian Processes Hwanwoo Kim D. Sanz-Alonso Ruiyi Yang 82 2 0 20 Oct 2022
Boundary-Aware Uncertainty for Feature Attribution Explainers Davin Hill A. Masoomi Max Torop S. Ghimire Jennifer Dy FAtt 147 3 0 05 Oct 2022
Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax Hao-Ren Yao Nairen Cao Katina Russell D. Chang O. Frieder Jeremy T. Fineman SSL 71 1 0 01 Sep 2022
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case I. Azangulov A. Smolensky Alexander Terenin Viacheslav Borovitskiy 97 21 0 31 Aug 2022
Crosslinguistic word order variation reflects evolutionary pressures of dependency and information locality Michael Hahn Yang Xu 49 18 0 09 Jun 2022
Time-inhomogeneous diffusion geometry and topology G. Huguet Alexander Tong Bastian Rieck Je-chun Huang Manik Kuchroo M. Hirn Guy Wolf Smita Krishnaswamy AI4CE 32 4 0 28 Mar 2022
Distribution Regression with Sliced Wasserstein Kernels Dimitri Meunier Massimiliano Pontil C. Ciliberto OOD 54 17 0 08 Feb 2022
Geodesic squared exponential kernel for non-rigid shape registration Florent Jousse Xavier Pennec H. Delingette M. González 21 3 0 22 Dec 2021
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels Noémie Jaquier Viacheslav Borovitskiy A. Smolensky Alexander Terenin Tamim Asfour Leonel Rozo 81 37 0 02 Nov 2021
Generalized Shape Metrics on Neural Representations Alex H. Williams Erin M Kunz Simon Kornblith Scott W. Linderman MedIm 65 105 0 27 Oct 2021
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels M. Hutchinson Alexander Terenin Viacheslav Borovitskiy So Takao Yee Whye Teh M. Deisenroth 68 22 0 27 Oct 2021
Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices F. López Beatrice Pozzetti Steve J. Trettel Michael Strube Anna Wienhard 72 17 0 26 Oct 2021
Dimension Reduction for Fréchet Regression Q. Zhang Lingzhou Xue Bing Li 69 15 0 01 Oct 2021
Marginalising over Stationary Kernels with Bayesian Quadrature Saad Hamid Sebastian Schulze Michael A. Osborne Stephen J. Roberts GP 55 4 0 14 Jun 2021
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions Leslie O’Bray Max Horn Bastian Rieck Karsten Borgwardt 112 41 0 02 Jun 2021
Inference for Gaussian Processes with Matérn Covariogram on Compact Riemannian Manifolds Didong Li Wenpin Tang Sudipto Banerjee 87 14 0 08 Apr 2021
Flow-based Generative Models for Learning Manifold to Manifold Mappings Xingjian Zhen Rudrasis Chakraborty Liu Yang Vikas Singh DRL MedIm 135 9 0 18 Dec 2020
Matérn Gaussian Processes on Graphs Viacheslav Borovitskiy I. Azangulov Alexander Terenin P. Mostowsky M. Deisenroth N. Durrande 53 82 0 29 Oct 2020
High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds Noémie Jaquier Leonel Rozo 103 24 0 21 Oct 2020
The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation Thibault Séjourné François-Xavier Vialard Gabriel Peyré OT 99 71 0 09 Sep 2020
Likelihood-Free Gaussian Process for Regression Yuta Shikuri 28 0 0 24 Jun 2020
Matérn Gaussian processes on Riemannian manifolds Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 128 123 0 17 Jun 2020
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data Kristopher T. Jensen Ta-Chu Kao Marco Tripodi Guillaume Hennequin DRL 84 32 0 12 Jun 2020
Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures Fanghui Liu Xiaolin Huang Yingyi Chen Johan A. K. Suykens 25 0 0 30 May 2020
Bayesian Optimization Meets Riemannian Manifolds in Robot Learning Noémie Jaquier Leonel Rozo Sylvain Calinon Mathias Bürger 75 54 0 11 Oct 2019