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Predictive Churn with the Set of Good Models

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
ArXivPDFHTML

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

10 / 10 papers shown
Title
Regretful Decisions under Label Noise
Regretful Decisions under Label Noise
Sujay Nagaraj
Yang Liu
Flavio du Pin Calmon
Berk Ustun
NoLa
40
1
0
12 Apr 2025
The Curious Case of Arbitrariness in Machine Learning
Prakhar Ganesh
Afaf Taik
G. Farnadi
39
0
0
28 Jan 2025
datadriftR: An R Package for Concept Drift Detection in Predictive
  Models
datadriftR: An R Package for Concept Drift Detection in Predictive Models
Ugur Dar
Mustafa Cavus
74
0
0
15 Dec 2024
Towards More Realistic Extraction Attacks: An Adversarial Perspective
Towards More Realistic Extraction Attacks: An Adversarial Perspective
Yash More
Prakhar Ganesh
G. Farnadi
AAML
46
4
0
02 Jul 2024
The Cost of Arbitrariness for Individuals: Examining the Legal and
  Technical Challenges of Model Multiplicity
The Cost of Arbitrariness for Individuals: Examining the Legal and Technical Challenges of Model Multiplicity
Prakhar Ganesh
Ihsan Ibrahim Daldaban
Ignacio Cofone
G. Farnadi
28
0
0
28 May 2024
Measuring and Reducing Model Update Regression in Structured Prediction
  for NLP
Measuring and Reducing Model Update Regression in Structured Prediction for NLP
Deng Cai
Elman Mansimov
Yi-An Lai
Yixuan Su
Lei Shu
Yi Zhang
KELM
31
7
0
07 Feb 2022
Knowledge Distillation by On-the-Fly Native Ensemble
Knowledge Distillation by On-the-Fly Native Ensemble
Xu Lan
Xiatian Zhu
S. Gong
181
436
0
12 Jun 2018
Large scale distributed neural network training through online
  distillation
Large scale distributed neural network training through online distillation
Rohan Anil
Gabriel Pereyra
Alexandre Passos
Róbert Ormándi
George E. Dahl
Geoffrey E. Hinton
FedML
260
381
0
09 Apr 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
268
4,940
0
05 Dec 2016
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
UQCV
BDL
240
8,157
0
06 Jun 2015
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