ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2002.03689
  4. Cited By
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings

A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings

10 February 2020
Junhyung Park
Krikamol Muandet
ArXivPDFHTML

Papers citing "A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings"

32 / 32 papers shown
Title
Conditional Dependence via U-Statistics Pruning
Conditional Dependence via U-Statistics Pruning
Ferran de Cabrera
Marc Vilà-Insa
Jaume Riba
28
0
0
21 Oct 2024
Unifying Invariant and Variant Features for Graph Out-of-Distribution
  via Probability of Necessity and Sufficiency
Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiency
Xuexin Chen
Ruichu Cai
Kaitao Zheng
Zhifan Jiang
Zhengting Huang
Zhifeng Hao
Zijian Li
54
0
0
21 Jul 2024
Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
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
44
8
0
28 Feb 2024
Kernel Single Proxy Control for Deterministic Confounding
Kernel Single Proxy Control for Deterministic Confounding
Liyuan Xu
A. Gretton
CML
26
2
0
08 Aug 2023
A continuous Structural Intervention Distance to compare Causal Graphs
A continuous Structural Intervention Distance to compare Causal Graphs
Mihir Dhanakshirur
F. Laumann
Junhyung Park
Mauricio Barahona
CML
24
3
0
31 Jul 2023
Causal survival embeddings: non-parametric counterfactual inference
  under censoring
Causal survival embeddings: non-parametric counterfactual inference under censoring
Carlos García-Meixide
Marcos Matabuena
CML
38
5
0
20 Jun 2023
Supervised learning with probabilistic morphisms and kernel mean
  embeddings
Supervised learning with probabilistic morphisms and kernel mean embeddings
H. Lê
GAN
18
1
0
10 May 2023
Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics
  Models
Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics Models
Jia-Jie Zhu
16
0
0
27 Apr 2023
Confidence and Uncertainty Assessment for Distributional Random Forests
Confidence and Uncertainty Assessment for Distributional Random Forests
Jeffrey Näf
Corinne Emmenegger
Peter Buhlmann
N. Meinshausen
32
3
0
11 Feb 2023
Returning The Favour: When Regression Benefits From Probabilistic Causal
  Knowledge
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
S. Bouabid
Jake Fawkes
Dino Sejdinovic
CML
44
0
0
26 Jan 2023
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making
Shujian Yu
Hongming Li
Sigurd Løkse
Robert Jenssen
José C. Príncipe
BDL
28
6
0
21 Jan 2023
Learning linear operators: Infinite-dimensional regression as a
  well-behaved non-compact inverse problem
Learning linear operators: Infinite-dimensional regression as a well-behaved non-compact inverse problem
Mattes Mollenhauer
Nicole Mücke
T. Sullivan
22
24
0
16 Nov 2022
Calibration tests beyond classification
Calibration tests beyond classification
David Widmann
Fredrik Lindsten
Dave Zachariah
30
17
0
21 Oct 2022
Minimax Optimal Kernel Operator Learning via Multilevel Training
Minimax Optimal Kernel Operator Learning via Multilevel Training
Jikai Jin
Yiping Lu
Jose H. Blanchet
Lexing Ying
23
11
0
28 Sep 2022
Optimal Rates for Regularized Conditional Mean Embedding Learning
Optimal Rates for Regularized Conditional Mean Embedding Learning
Zhu Li
D. Meunier
Mattes Mollenhauer
A. Gretton
30
46
0
02 Aug 2022
From Statistical to Causal Learning
From Statistical to Causal Learning
Bernhard Schölkopf
Julius von Kügelgen
CML
28
45
0
01 Apr 2022
Generalized Label Shift Correction via Minimum Uncertainty Principle:
  Theory and Algorithm
Generalized Label Shift Correction via Minimum Uncertainty Principle: Theory and Algorithm
You-Wei Luo
Chuan-Xian Ren
25
2
0
26 Feb 2022
Towards Empirical Process Theory for Vector-Valued Functions: Metric
  Entropy of Smooth Function Classes
Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes
Junhyung Park
Krikamol Muandet
19
6
0
09 Feb 2022
Data-Driven Chance Constrained Control using Kernel Distribution
  Embeddings
Data-Driven Chance Constrained Control using Kernel Distribution Embeddings
Adam J. Thorpe
T. Lew
Meeko Oishi
Marco Pavone
25
21
0
08 Feb 2022
Local permutation tests for conditional independence
Local permutation tests for conditional independence
Ilmun Kim
Matey Neykov
Sivaraman Balakrishnan
Larry A. Wasserman
13
27
0
22 Dec 2021
An Asymptotic Test for Conditional Independence using Analytic Kernel
  Embeddings
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings
M. Scetbon
Laurent Meunier
Yaniv Romano
25
10
0
28 Oct 2021
RKHS-SHAP: Shapley Values for Kernel Methods
RKHS-SHAP: Shapley Values for Kernel Methods
Siu Lun Chau
Robert Hu
Javier I. González
Dino Sejdinovic
FAtt
17
15
0
18 Oct 2021
Sobolev Norm Learning Rates for Conditional Mean Embeddings
Sobolev Norm Learning Rates for Conditional Mean Embeddings
Prem M. Talwai
A. Shameli
D. Simchi-Levi
24
10
0
16 May 2021
Exact Distribution-Free Hypothesis Tests for the Regression Function of
  Binary Classification via Conditional Kernel Mean Embeddings
Exact Distribution-Free Hypothesis Tests for the Regression Function of Binary Classification via Conditional Kernel Mean Embeddings
Ambrus Tamás
Balázs Csanád Csáji
25
4
0
08 Mar 2021
Conditional Distributional Treatment Effect with Kernel Conditional Mean
  Embeddings and U-Statistic Regression
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
Junhyung Park
Uri Shalit
Bernhard Schölkopf
Krikamol Muandet
CML
19
31
0
16 Feb 2021
Barking up the right tree: an approach to search over molecule synthesis
  DAGs
Barking up the right tree: an approach to search over molecule synthesis DAGs
John Bradshaw
Brooks Paige
Matt J. Kusner
Marwin H. S. Segler
José Miguel Hernández-Lobato
51
56
0
21 Dec 2020
Kernel Methods for Causal Functions: Dose, Heterogeneous, and
  Incremental Response Curves
Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves
Rahul Singh
Liyuan Xu
A. Gretton
OffRL
55
26
0
10 Oct 2020
Distributional Random Forests: Heterogeneity Adjustment and Multivariate
  Distributional Regression
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression
Domagoj Cevid
Loris Michel
Jeffrey Näf
N. Meinshausen
Peter Buhlmann
35
39
0
29 May 2020
Constrained Polynomial Likelihood
Constrained Polynomial Likelihood
Caio Almeida
Ricardo Masini
P. Schneider
12
4
0
30 Oct 2019
Consistent Kernel Mean Estimation for Functions of Random Variables
Consistent Kernel Mean Estimation for Functions of Random Variables
Carl-Johann Simon-Gabriel
Adam Scibior
Ilya O. Tolstikhin
Bernhard Schölkopf
32
14
0
19 Oct 2016
A Kernel Test of Goodness of Fit
A Kernel Test of Goodness of Fit
Kacper P. Chwialkowski
Heiko Strathmann
A. Gretton
BDL
107
324
0
09 Feb 2016
Conditional mean embeddings as regressors - supplementary
Conditional mean embeddings as regressors - supplementary
Steffen Grunewalder
Guy Lever
Luca Baldassarre
Sam Patterson
A. Gretton
Massimiliano Pontil
85
143
0
21 May 2012
1