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Distribution-Free Distribution Regression

Distribution-Free Distribution Regression

1 February 2013
Barnabás Póczós
Alessandro Rinaldo
Aarti Singh
Larry A. Wasserman
ArXiv (abs)PDFHTML

Papers citing "Distribution-Free Distribution Regression"

47 / 47 papers shown
Title
Bayesian Density-Density Regression with Application to Cell-Cell Communications
Bayesian Density-Density Regression with Application to Cell-Cell Communications
Khai Nguyen
Yang Ni
Peter Mueller
89
0
0
17 Apr 2025
Improved learning theory for kernel distribution regression with two-stage sampling
Improved learning theory for kernel distribution regression with two-stage sampling
François Bachoc
Louis Bethune
Alberto González Sanz
Jean-Michel Loubes
145
2
0
28 Jan 2025
Scalable Signature-Based Distribution Regression via Reference Sets
Scalable Signature-Based Distribution Regression via Reference Sets
Andrew Alden
Carmine Ventre
Blanka Horvath
142
0
0
11 Oct 2024
Learning to Embed Distributions via Maximum Kernel Entropy
Learning to Embed Distributions via Maximum Kernel Entropy
Oleksii Kachaiev
Stefano Recanatesi
OOD
120
0
0
01 Aug 2024
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman
  graph kernels
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
Raphael Carpintero Perez
Sébastien da Veiga
Josselin Garnier
B. Staber
107
6
0
06 Feb 2024
LearnedWMP: Workload Memory Prediction Using Distribution of Query
  Templates
LearnedWMP: Workload Memory Prediction Using Distribution of Query Templates
Shaikh Quader
Andres Jaramillo
Sumona Mukhopadhyay
Ghadeer Abuoda
Calisto Zuzarte
David Kalmuk
Marin Litoiu
Manos Papagelis
33
1
0
22 Jan 2024
Learning Theory of Distribution Regression with Neural Networks
Learning Theory of Distribution Regression with Neural Networks
Zhongjie Shi
Zhan Yu
Ding-Xuan Zhou
32
2
0
07 Jul 2023
Statistical learning on measures: an application to persistence diagrams
Statistical learning on measures: an application to persistence diagrams
Olympio Hacquard
Gilles Blanchard
Clément Levrard
134
3
0
15 Mar 2023
Direct Bayesian Regression for Distribution-valued Covariates
Direct Bayesian Regression for Distribution-valued Covariates
Bohao Tang
Sandipan Pramanik
Yi Zhao
B. Caffo
A. Datta
39
0
0
11 Mar 2023
Gaussian Processes on Distributions based on Regularized Optimal
  Transport
Gaussian Processes on Distributions based on Regularized Optimal Transport
François Bachoc
Louis Bethune
Alberto González Sanz
Jean-Michel Loubes
GPOT
66
8
0
12 Oct 2022
Coefficient-based Regularized Distribution Regression
Coefficient-based Regularized Distribution Regression
Yuan Mao
Lei Shi
Zheng-Chu Guo
118
4
0
26 Aug 2022
Generalized Identifiability Bounds for Mixture Models with Grouped
  Samples
Generalized Identifiability Bounds for Mixture Models with Grouped Samples
Robert A. Vandermeulen
René Saitenmacher
72
3
0
22 Jul 2022
Performance of Distribution Regression with Doubling Measure under the
  seek of Closest Point
Performance of Distribution Regression with Doubling Measure under the seek of Closest Point
Ilqar Ramazanli
52
3
0
01 Mar 2022
Distribution Regression with Sliced Wasserstein Kernels
Distribution Regression with Sliced Wasserstein Kernels
Dimitri Meunier
Massimiliano Pontil
C. Ciliberto
OOD
72
17
0
08 Feb 2022
Label-Free Model Evaluation with Semi-Structured Dataset Representations
Label-Free Model Evaluation with Semi-Structured Dataset Representations
Xiaoxiao Sun
Yunzhong Hou
Hongdong Li
Liang Zheng
66
12
0
01 Dec 2021
A Gentle Introduction to Conformal Prediction and Distribution-Free
  Uncertainty Quantification
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
Anastasios Nikolas Angelopoulos
Stephen Bates
OOD
271
625
0
15 Jul 2021
Robust Kernel-based Distribution Regression
Robust Kernel-based Distribution Regression
Zhan Yu
D. Ho
Ding-Xuan Zhou
OOD
68
11
0
21 Apr 2021
Nonlinear Distribution Regression for Remote Sensing Applications
Nonlinear Distribution Regression for Remote Sensing Applications
J. Adsuara
Adrián Pérez-Suay
Jordi Munoz-Marí
Anna Mateo-Sanchis
M. Piles
Gustau Camps-Valls
102
17
0
07 Dec 2020
Causal inference using deep neural networks
Causal inference using deep neural networks
Ye Yuan
Xueying Ding
Z. Bar-Joseph
BDLCML
50
2
0
25 Nov 2020
Stochastic Gradient Descent Meets Distribution Regression
Stochastic Gradient Descent Meets Distribution Regression
Nicole Mücke
62
5
0
24 Oct 2020
Anomaly Detection at Scale: The Case for Deep Distributional Time Series
  Models
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models
Fadhel Ayed
Lorenzo Stella
Tim Januschowski
Jan Gasthaus
AI4TS
103
10
0
30 Jul 2020
Estimates on Learning Rates for Multi-Penalty Distribution Regression
Estimates on Learning Rates for Multi-Penalty Distribution Regression
Zhan Yu
D. Ho
23
0
0
16 Jun 2020
Deep Kernels with Probabilistic Embeddings for Small-Data Learning
Deep Kernels with Probabilistic Embeddings for Small-Data Learning
Ankur Mallick
Chaitanya Dwivedi
B. Kailkhura
Gauri Joshi
T. Y. Han
BDLUQCV
44
8
0
13 Oct 2019
Dataset2Vec: Learning Dataset Meta-Features
Dataset2Vec: Learning Dataset Meta-Features
H. Jomaa
Lars Schmidt-Thieme
Josif Grabocka
SSL
86
64
0
27 May 2019
3D Object Recognition with Ensemble Learning --- A Study of Point
  Cloud-Based Deep Learning Models
3D Object Recognition with Ensemble Learning --- A Study of Point Cloud-Based Deep Learning Models
Daniel Koguciuk
L. Chechlinski
Tarek El-Gaaly
3DPC
48
11
0
17 Apr 2019
On Deep Set Learning and the Choice of Aggregations
On Deep Set Learning and the Choice of Aggregations
Maximilian Sölch
A. Akhundov
Patrick van der Smagt
Justin Bayer
TDI
72
20
0
18 Mar 2019
Theoretical and Experimental Analysis on the Generalizability of
  Distribution Regression Network
Theoretical and Experimental Analysis on the Generalizability of Distribution Regression Network
C. Kou
H. Lee
Jorge Sanz
Teck Khim Ng
27
0
0
05 Nov 2018
Distribution regression model with a Reproducing Kernel Hilbert Space
  approach
Distribution regression model with a Reproducing Kernel Hilbert Space approach
T. T. T. Bui
Jean-Michel Loubes
Risser
Balaresque
58
11
0
27 Jun 2018
Causal effects based on distributional distances
Causal effects based on distributional distances
Kwangho Kim
Jisu Kim
Edward H. Kennedy
CML
56
19
0
08 Jun 2018
A Compact Network Learning Model for Distribution Regression
A Compact Network Learning Model for Distribution Regression
C. Kou
H. Lee
Teck Khim Ng
64
10
0
13 Apr 2018
Kernel Embedding Approaches to Orbit Determination of Spacecraft
  Clusters
Kernel Embedding Approaches to Orbit Determination of Spacecraft Clusters
Srinagesh Sharma
J. Cutler
15
3
0
01 Mar 2018
Distance Measure Machines
Distance Measure Machines
A. Rakotomamonjy
Abraham Traoré
Maxime Bérar
Rémi Flamary
Nicolas Courty
64
12
0
01 Mar 2018
Estimating Cosmological Parameters from the Dark Matter Distribution
Estimating Cosmological Parameters from the Dark Matter Distribution
Siamak Ravanbakhsh
Junier Oliva
S. Fromenteau
Layne Price
S. Ho
J. Schneider
Barnabás Póczós
78
76
0
06 Nov 2017
Bayesian Approaches to Distribution Regression
Bayesian Approaches to Distribution Regression
H. Law
Danica J. Sutherland
Dino Sejdinovic
Seth Flaxman
OODUQCVBDL
91
37
0
11 May 2017
Deep Sets
Deep Sets
Manzil Zaheer
Satwik Kottur
Siamak Ravanbakhsh
Barnabás Póczós
Ruslan Salakhutdinov
Alex Smola
481
2,485
0
10 Mar 2017
A Gaussian Process Regression Model for Distribution Inputs
A Gaussian Process Regression Model for Distribution Inputs
François Bachoc
Fabrice Gamboa
Jean-Michel Loubes
N. Venet
106
53
0
31 Jan 2017
Prototypal Analysis and Prototypal Regression
Prototypal Analysis and Prototypal Regression
Chenyue Wu
E. Tabak
63
13
0
31 Jan 2017
From Dependence to Causation
From Dependence to Causation
David Lopez-Paz
OODCML
179
26
0
12 Jul 2016
Uncertain programming model for multi-item solid transportation problem
Uncertain programming model for multi-item solid transportation problem
Hasan Dalman
155
64
0
31 May 2016
Operator-valued Kernels for Learning from Functional Response Data
Operator-valued Kernels for Learning from Functional Response Data
Hachem Kadri
E. Duflos
Philippe Preux
S. Canu
A. Rakotomamonjy
Julien Audiffren
93
131
0
28 Oct 2015
Linear-time Learning on Distributions with Approximate Kernel Embeddings
Linear-time Learning on Distributions with Approximate Kernel Embeddings
Danica J. Sutherland
Junier B. Oliva
Barnabás Póczós
J. Schneider
61
18
0
24 Sep 2015
On The Identifiability of Mixture Models from Grouped Samples
On The Identifiability of Mixture Models from Grouped Samples
Robert A. Vandermeulen
Clayton D. Scott
45
9
0
23 Feb 2015
Passing Expectation Propagation Messages with Kernel Methods
Passing Expectation Propagation Messages with Kernel Methods
Wittawat Jitkrittum
Arthur Gretton
N. Heess
27
0
0
02 Jan 2015
Learning Theory for Distribution Regression
Learning Theory for Distribution Regression
Z. Szabó
Bharath K. Sriperumbudur
Barnabás Póczós
Arthur Gretton
OOD
116
140
0
08 Nov 2014
Two-stage Sampled Learning Theory on Distributions
Two-stage Sampled Learning Theory on Distributions
Z. Szabó
Arthur Gretton
Barnabás Póczós
Bharath K. Sriperumbudur
OOD
61
3
0
07 Feb 2014
Fast Distribution To Real Regression
Fast Distribution To Real Regression
Junier B. Oliva
Willie Neiswanger
Barnabás Póczós
J. Schneider
Eric Xing
91
45
0
10 Nov 2013
FuSSO: Functional Shrinkage and Selection Operator
FuSSO: Functional Shrinkage and Selection Operator
Junier B. Oliva
Barnabás Póczós
Timothy D. Verstynen
Aarti Singh
J. Schneider
F. Yeh
W. Tseng
42
10
0
10 Nov 2013
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