Papers
Communities
Events
Blog
Pricing
Search
Open menu
Home
Papers
1604.05251
Cited By
v1
v2 (latest)
Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions
18 April 2016
Carl-Johann Simon-Gabriel
Bernhard Schölkopf
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions"
50 / 63 papers shown
Title
On relative universality, regression operator, and conditional independence
Bing Li
Ben Jones
A. Artemiou
52
0
0
15 Apr 2025
Towards Scalable Topological Regularizers
Hiu-Tung Wong
Darrick Lee
Hong Yan
BDL
137
0
0
24 Jan 2025
Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian Processes
Hugh Dance
Peter Orbanz
Arthur Gretton
CML
61
1
0
18 Oct 2024
Gaussian Processes for Observational Dose-Response Inference
Jake R. Dailey
45
0
0
25 Sep 2024
Learning to Embed Distributions via Maximum Kernel Entropy
Oleksii Kachaiev
Stefano Recanatesi
OOD
120
0
0
01 Aug 2024
Structurally Aware Robust Model Selection for Mixtures
Jiawei Li
Jonathan H. Huggins
81
0
0
01 Mar 2024
Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
Georg Manten
Cecilia Casolo
E. Ferrucci
Søren Wengel Mogensen
C. Salvi
Niki Kilbertus
CML
BDL
248
12
0
28 Feb 2024
Transfer Operators from Batches of Unpaired Points via Entropic Transport Kernels
F. Beier
Hancheng Bi
Clément Sarrazin
Bernhard Schmitzer
Gabriele Steidl
OT
40
2
0
13 Feb 2024
Wasserstein Gradient Flows for Moreau Envelopes of f-Divergences in Reproducing Kernel Hilbert Spaces
Viktor Stein
Sebastian Neumayer
Gabriele Steidl
Nicolaj Rux
164
11
0
07 Feb 2024
Transportation Marketplace Rate Forecast Using Signature Transform
Haotian Gu
Xin Guo
Timothy L. Jacobs
Philip M. Kaminsky
Xinyu Li
AI4TS
50
0
0
10 Jan 2024
Optimal Transport for Kernel Gaussian Mixture Models
Jung Hun Oh
Rena Elkin
Anish K. Simhal
Jiening Zhu
Joseph O. Deasy
Allen Tannenbaum
OT
61
0
0
28 Oct 2023
Geometric Learning with Positively Decomposable Kernels
Nathael Da Costa
Cyrus Mostajeran
Juan-Pablo Ortega
Salem Said
64
3
0
20 Oct 2023
Spectral Regularized Kernel Goodness-of-Fit Tests
Omar Hagrass
Bharath K. Sriperumbudur
Bing Li
143
4
0
08 Aug 2023
Kernel-Based Testing for Single-Cell Differential Analysis
Anthony Ozier-Lafontaine
Camille Fourneaux
G. Durif
Polina Arsenteva
C. Vallot
O. Gandrillon
Sandrine Giraud
Bertrand Michel
Franck Picard
55
6
0
17 Jul 2023
Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures
Zacharia Issa
Blanka Horvath
97
4
0
27 Jun 2023
Non-adversarial training of Neural SDEs with signature kernel scores
Zacharia Issa
Blanka Horvath
M. Lemercier
C. Salvi
AI4TS
116
26
0
25 May 2023
Estimation Beyond Data Reweighting: Kernel Method of Moments
Heiner Kremer
Yassine Nemmour
Bernhard Schölkopf
Jia-Jie Zhu
103
7
0
18 May 2023
Efficient Convex Algorithms for Universal Kernel Learning
Aleksandr Talitckii
Brendon K. Colbert
Matthew M. Peet
18
1
0
15 Apr 2023
Robustifying likelihoods by optimistically re-weighting data
Miheer Dewaskar
Christopher Tosh
Jeremias Knoblauch
David B. Dunson
93
6
0
19 Mar 2023
Reproducing kernel Hilbert spaces in the mean field limit
Christian Fiedler
Michael Herty
M. Rom
C. Segala
Sebastian Trimpe
71
6
0
28 Feb 2023
Spectral Regularized Kernel Two-Sample Tests
Omar Hagrass
Bharath K. Sriperumbudur
Bing Li
78
15
0
19 Dec 2022
Targeted Separation and Convergence with Kernel Discrepancies
Alessandro Barp
Carl-Johann Simon-Gabriel
Mark Girolami
Lester W. Mackey
149
15
0
26 Sep 2022
Ensemble forecasts in reproducing kernel Hilbert space family
Benjamin Dufée
Berenger Hug
É. Mémin
G. Tissot
138
1
0
29 Jul 2022
Learning Counterfactually Invariant Predictors
Francesco Quinzan
Cecilia Casolo
Krikamol Muandet
Yucen Luo
Niki Kilbertus
118
10
0
20 Jul 2022
Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures
Johanna Fasciati-Ziegel
D. Ginsbourger
L. Dümbgen
VLM
32
6
0
15 Jun 2022
Robust Flow-based Conformal Inference (FCI) with Statistical Guarantee
Youhui Ye
Meimei Liu
Xin Xing
59
0
0
22 May 2022
On boundary conditions parametrized by analytic functions
Markus Lange-Hegermann
D. Robertz
54
5
0
06 May 2022
Kernel Robust Hypothesis Testing
Zhongchang Sun
Shaofeng Zou
OOD
77
13
0
23 Mar 2022
Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents
Alessandro Barp
Lancelot Da Costa
G. Francca
Karl J. Friston
Mark Girolami
Michael I. Jordan
G. Pavliotis
97
25
0
20 Mar 2022
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support
Pierre Glaser
Michael Arbel
Arthur Gretton
131
40
0
16 Jun 2021
Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey
Benyamin Ghojogh
A. Ghodsi
Fakhri Karray
Mark Crowley
72
37
0
15 Jun 2021
Statistical Depth Meets Machine Learning: Kernel Mean Embeddings and Depth in Functional Data Analysis
George Wynne
Stanislav Nagy
103
6
0
26 May 2021
Robust Sample Weighting to Facilitate Individualized Treatment Rule Learning for a Target Population
Rui Chen
J. Huling
Guanhua Chen
Menggang Yu
CML
64
1
0
03 May 2021
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
Junhyung Park
Uri Shalit
Bernhard Schölkopf
Krikamol Muandet
CML
115
33
0
16 Feb 2021
Handling Hard Affine SDP Shape Constraints in RKHSs
Pierre-Cyril Aubin-Frankowski
Z. Szabó
49
7
0
05 Jan 2021
Glucose values prediction five years ahead with a new framework of missing responses in reproducing kernel Hilbert spaces, and the use of continuous glucose monitoring technology
Marcos Matabuena
Paulo Félix
C. Meijide-García
F. Gudé
105
0
0
11 Dec 2020
Kernel Mean Embedding of Probability Measures and its Applications to Functional Data Analysis
Saeed Hayati
Kenji Fukumizu
A. Parvardeh
24
5
0
04 Nov 2020
Instrumental Variable Regression via Kernel Maximum Moment Loss
Rui Zhang
Masaaki Imaizumi
Bernhard Schölkopf
Krikamol Muandet
93
9
0
15 Oct 2020
Unbalanced Sobolev Descent
Youssef Mroueh
Mattia Rigotti
70
17
0
29 Sep 2020
A Kernel Two-Sample Test for Functional Data
George Wynne
Andrew B. Duncan
99
45
0
25 Aug 2020
Path Signatures on Lie Groups
Darrick Lee
Robert Ghrist
78
8
0
02 Jul 2020
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression
Domagoj Cevid
Loris Michel
Jeffrey Näf
N. Meinshausen
Peter Buhlmann
96
43
0
29 May 2020
Hard Shape-Constrained Kernel Machines
Pierre-Cyril Aubin-Frankowski
Z. Szabó
62
22
0
26 May 2020
Generalized Energy Based Models
Michael Arbel
Liang Zhou
Arthur Gretton
DRL
179
81
0
10 Mar 2020
Kernel Conditional Moment Test via Maximum Moment Restriction
Krikamol Muandet
Wittawat Jitkrittum
Jonas M. Kubler
54
25
0
21 Feb 2020
Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization
Poompol Buathong
D. Ginsbourger
Tipaluck Krityakierne
BDL
97
22
0
09 Oct 2019
Comparing distributions:
ℓ
1
\ell_1
ℓ
1
geometry improves kernel two-sample testing
M. Scetbon
Gaël Varoquaux
61
10
0
19 Sep 2019
Asymptotically Optimal One- and Two-Sample Testing with Kernels
Shengyu Zhu
Biao Chen
Zhitang Chen
Pengfei Yang
78
7
0
27 Aug 2019
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
Csaba Tóth
Harald Oberhauser
48
9
0
19 Jun 2019
A Reproducing Kernel Hilbert Space log-rank test for the two-sample problem
T. Fernandez
Nicolás Rivera
41
8
0
10 Apr 2019
1
2
Next