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Pushing the Accuracy-Group Robustness Frontier with Introspective
  Self-play

Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play

11 February 2023
J. Liu
Krishnamurthy Dvijotham
Jihyeon Janel Lee
Quan Yuan
Martin Strobel
Balaji Lakshminarayanan
Deepak Ramachandran
ArXivPDFHTML

Papers citing "Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play"

17 / 17 papers shown
Title
Revisiting the Importance of Amplifying Bias for Debiasing
Revisiting the Importance of Amplifying Bias for Debiasing
Jungsoo Lee
Jeonghoon Park
Daeyoung Kim
Juyoung Lee
E. Choi
Jaegul Choo
37
21
0
29 May 2022
Fair Representation Learning through Implicit Path Alignment
Fair Representation Learning through Implicit Path Alignment
Changjian Shui
Qi Chen
Jiaqi Li
Boyu Wang
Christian Gagné
36
28
0
26 May 2022
Conditional Supervised Contrastive Learning for Fair Text Classification
Conditional Supervised Contrastive Learning for Fair Text Classification
Jianfeng Chi
Will Shand
Yaodong Yu
Kai-Wei Chang
Han Zhao
Yuan Tian
FaML
41
14
0
23 May 2022
Adaptive Sampling Strategies to Construct Equitable Training Datasets
Adaptive Sampling Strategies to Construct Equitable Training Datasets
William Cai
R. Encarnación
Bobbie Chern
S. Corbett-Davies
Miranda Bogen
Stevie Bergman
Sharad Goel
77
29
0
31 Jan 2022
Simple and near-optimal algorithms for hidden stratification and
  multi-group learning
Simple and near-optimal algorithms for hidden stratification and multi-group learning
Abdoreza Asadpour
Daniel J. Hsu
89
20
0
22 Dec 2021
Learning Fair Classifiers with Partially Annotated Group Labels
Learning Fair Classifiers with Partially Annotated Group Labels
Sangwon Jung
Sanghyuk Chun
Taesup Moon
60
46
0
29 Nov 2021
Contrastive Learning for Fair Representations
Contrastive Learning for Fair Representations
Aili Shen
Xudong Han
Trevor Cohn
Timothy Baldwin
Lea Frermann
FaML
34
33
0
22 Sep 2021
On the Impact of Spurious Correlation for Out-of-distribution Detection
On the Impact of Spurious Correlation for Out-of-distribution Detection
Yifei Ming
Hang Yin
Yixuan Li
OODD
144
74
0
12 Sep 2021
Correlated Input-Dependent Label Noise in Large-Scale Image
  Classification
Correlated Input-Dependent Label Noise in Large-Scale Image Classification
Mark Collier
Basil Mustafa
Efi Kokiopoulou
Rodolphe Jenatton
Jesse Berent
NoLa
176
53
0
19 May 2021
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for
  Out-of-Distribution Robustness
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness
Sang Michael Xie
Ananya Kumar
Robbie Jones
Fereshte Khani
Tengyu Ma
Percy Liang
OOD
153
62
0
08 Dec 2020
An Investigation of Why Overparameterization Exacerbates Spurious
  Correlations
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
Shiori Sagawa
Aditi Raghunathan
Pang Wei Koh
Percy Liang
144
369
0
09 May 2020
Equalization Loss for Long-Tailed Object Recognition
Equalization Loss for Long-Tailed Object Recognition
Jingru Tan
Changbao Wang
Buyu Li
Quanquan Li
Wanli Ouyang
Changqing Yin
Junjie Yan
237
455
0
11 Mar 2020
Out-of-Distribution Generalization via Risk Extrapolation (REx)
Out-of-Distribution Generalization via Risk Extrapolation (REx)
David M. Krueger
Ethan Caballero
J. Jacobsen
Amy Zhang
Jonathan Binas
Dinghuai Zhang
Rémi Le Priol
Aaron Courville
OOD
215
898
0
02 Mar 2020
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
294
4,187
0
23 Aug 2019
Learning Adversarially Fair and Transferable Representations
Learning Adversarially Fair and Transferable Representations
David Madras
Elliot Creager
T. Pitassi
R. Zemel
FaML
210
669
0
17 Feb 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
5,652
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
247
9,109
0
06 Jun 2015
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