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Predictive Churn with the Set of Good Models
v1v2 (latest)

Predictive Churn with the Set of Good Models

12 February 2024
J. Watson-Daniels
Flavio du Pin Calmon
Alexander DÁmour
Carol Xuan Long
David C. Parkes
Berk Ustun
ArXiv (abs)PDFHTML

Papers citing "Predictive Churn with the Set of Good Models"

7 / 57 papers shown
Title
Satisfying Real-world Goals with Dataset Constraints
Satisfying Real-world Goals with Dataset Constraints
Gabriel Goh
Andrew Cotter
Maya R. Gupta
M. Friedlander
OffRL
60
215
0
24 Jun 2016
Towards Open Set Deep Networks
Towards Open Set Deep Networks
Abhijit Bendale
Terrance Boult
BDLEDL
104
1,427
0
19 Nov 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCVBDL
829
9,318
0
06 Jun 2015
Weight Uncertainty in Neural Networks
Weight Uncertainty in Neural Networks
Charles Blundell
Julien Cornebise
Koray Kavukcuoglu
Daan Wierstra
UQCVBDL
187
1,887
0
20 May 2015
Scalable Bayesian Optimization Using Deep Neural Networks
Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek
Oren Rippel
Kevin Swersky
Ryan Kiros
N. Satish
N. Sundaram
Md. Mostofa Ali Patwary
P. Prabhat
Ryan P. Adams
BDLUQCV
95
1,043
0
19 Feb 2015
Open science in machine learning
Open science in machine learning
Joaquin Vanschoren
M. Braun
Cheng Soon Ong
VLMAI4CE
46
13
0
24 Feb 2014
Manifold Gaussian Processes for Regression
Manifold Gaussian Processes for Regression
Roberto Calandra
Jan Peters
C. Rasmussen
M. Deisenroth
170
273
0
24 Feb 2014
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