Projects
Reading
People
Chat
SU\G
(𝔸)
/K·U
Projects
Reading
People
Chat
Sign Up
Light
Dark
System
Mind the Nuisance: Gaussian Process Classification using Privileged Noise
Daniel Hernández-Lobato
,
Viktoriia Sharmanska
,
Kristian Kersting
,
Christoph H. Lampert
,
Novi Quadrianto
Type:
Article
Publication Date:
2014-12-08
Citations:
12
View Publication
Share
Locations
arXiv (Cornell University) -
View
Similar Works
Action
Title
Year
Authors
+
Mind the Nuisance: Gaussian Process Classification using Privileged Noise
2014
Daniel Hernández-Lobato
Viktoriia Sharmanska
Kristian Kersting
Christoph H. Lampert
Novi Quadrianto
+
Mind the Nuisance: Gaussian Process Classification using Privileged Noise
2014
Daniel Hernández-Lobato
Viktoriia Sharmanska
Kristian Kersting
Christoph H. Lampert
Novi Quadrianto
+
Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer
2018
Ryosuke Kamesawa
Issei Sato
Masashi Sugiyama
+
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
2019
Théo Galy-Fajou
Florian Wenzel
Christian Donner
Manfred Opper
+
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
2019
Théo Galy-Fajou
Florian Wenzel
Christian Donner
Manfred Opper
+
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing
2023
Zi Wang
Alexander Ku
Jason Baldridge
Thomas L. Griffiths
Been Kim
+
Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods
2019
Haitao Liu
Yew-Soon Ong
Ziwei Yu
Jianfei Cai
Xiaobo Shen
+
PDF
Chat
Conditional Deep Gaussian Processes: Empirical Bayes Hyperdata Learning
2021
Chi-Ken Lu
Patrick Shafto
+
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
2018
David Reeb
Andreas Doerr
Sebastian Gerwinn
Barbara Rakitsch
+
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
2018
David Reeb
Andreas Doerr
Sebastian Gerwinn
Barbara Rakitsch
+
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
2018
David Reeb
Andreas Doerr
Sebastian Gerwinn
Barbara Rakitsch
+
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds
2022
Rui Li
St. John
Arno Solin
+
The Promises and Pitfalls of Deep Kernel Learning
2021
Sebastian W. Ober
Carl Edward Rasmussen
Mark van der Wilk
+
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning.
2021
Chi-Ken Lu
Patrick Shafto
+
Guided Deep Kernel Learning
2023
Idan Achituve
Gal Chechik
Ethan Fetaya
+
The Promises and Pitfalls of Deep Kernel Learning
2021
Sebastian W. Ober
Carl Edward Rasmussen
Mark van der Wilk
+
PDF
Chat
An Intuitive Tutorial to Gaussian Process Regression
2023
J. Wang
+
An Intuitive Tutorial to Gaussian Processes Regression
2020
J. Wang
+
Multi-class Gaussian Process Classification with Noisy Inputs
2020
Carlos Villacampa-Calvo
Bryan ZaldĂvar
Eduardo C. Garrido-Merchán
Daniel Hernández-Lobato
+
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
2020
Jakob Lindinger
David Reeb
Christoph Lippert
Barbara Rakitsch
Works That Cite This (7)
Action
Title
Year
Authors
+
PDF
Chat
Correlated Input-Dependent Label Noise in Large-Scale Image Classification
2021
Mark Collier
Basil Mustafa
Efi Kokiopoulou
Rodolphe Jenatton
Jesse Berent
+
Multi-class Gaussian Process Classification with Noisy Inputs
2020
Carlos Villacampa-Calvo
Bryan ZaldĂvar
Eduardo C. Garrido-Merchán
Daniel Hernández-Lobato
+
A Simple Probabilistic Method for Deep Classification under Input-Dependent Label Noise
2020
Mark Collier
Basil Mustafa
Efi Kokiopoulou
Rodolphe Jenatton
Jesse Berent
+
PDF
Chat
Robot Learning With Crash Constraints
2021
Alonso Marco
Dominik Baumann
Majid Khadiv
Philipp Hennig
Ludovic Righetti
Sebastian Trimpe
+
Learning to Transfer Privileged Information.
2014
Viktoriia Sharmanska
Novi Quadrianto
Christoph H. Lampert
+
Correlated Input-Dependent Label Noise in Large-Scale Image Classification
2021
Mark Collier
Basil Mustafa
Efi Kokiopoulou
Rodolphe Jenatton
Jesse Berent
+
PDF
Chat
Deep Learning Under Privileged Information Using Heteroscedastic Dropout
2018
John Lambert
Ozan Ĺžener
Silvio Savarese
Works Cited by This (7)
Action
Title
Year
Authors
+
Statistical Comparisons of Classifiers over Multiple Data Sets
2006
Janez Demšar
+
Nested expectation propagation for Gaussian process classification
2013
Jaakko Riihimäki
Pasi Jylänki
Aki Vehtari
+
PDF
Chat
Privileged information for data clustering
2011
Jan Feyereisl
Uwe Aickelin
+
PDF
Chat
Kernel Conditional Quantile Estimation via Reduction Revisited
2009
Novi Quadrianto
Kristian Kersting
Mark D. Reid
Tibério S. Caetano
Wray Buntine
+
Learning to Transfer Privileged Information.
2014
Viktoriia Sharmanska
Novi Quadrianto
Christoph H. Lampert
+
PDF
Chat
Learning using privileged information: SVM+ and weighted SVM
2014
Maksim Lapin
Matthias Hein
Bernt Schiele
+
Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood
2012
Jaakko Riihimäki
Pasi Jylänki
Aki Vehtari