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To Split or Not to Split: The Impact of Disparate Treatment in Classification
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To Split or Not to Split: The Impact of Disparate Treatment in Classification

IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2020
Hao Wang
Hsiang Hsu
Mario Díaz
Flavio du Pin Calmon
ArXiv (abs)PDFHTML

Papers citing "To Split or Not to Split: The Impact of Disparate Treatment in Classification"

12 / 12 papers shown
Title
To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models
To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models
Cyrus Cousins
I. E. Kumar
Suresh Venkatasubramanian
FedML
123
2
0
29 Feb 2024
Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition
Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition
Faisal Hamman
Sanghamitra Dutta
FedML
183
13
0
21 Jul 2023
Quantifying Feature Contributions to Overall Disparity Using Information Theory
Quantifying Feature Contributions to Overall Disparity Using Information Theory
Sanghamitra Dutta
Praveen Venkatesh
P. Grover
FAtt
86
5
0
16 Jun 2022
When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction
When Personalization Harms: Reconsidering the Use of Group Attributes in PredictionInternational Conference on Machine Learning (ICML), 2022
Vinith Suriyakumar
Marzyeh Ghassemi
Berk Ustun
200
9
0
04 Jun 2022
Can Information Flows Suggest Targets for Interventions in Neural Circuits?
Can Information Flows Suggest Targets for Interventions in Neural Circuits?Neural Information Processing Systems (NeurIPS), 2021
Praveen Venkatesh
Sanghamitra Dutta
Neil Mehta
P. Grover
AAML
118
8
0
09 Nov 2021
Algorithmic encoding of protected characteristics in image-based models for disease detection
Algorithmic encoding of protected characteristics in image-based models for disease detection
Ben Glocker
Charles Jones
Mélanie Bernhardt
S. Winzeck
249
10
0
27 Oct 2021
Optimality and Stability in Federated Learning: A Game-theoretic Approach
Optimality and Stability in Federated Learning: A Game-theoretic ApproachNeural Information Processing Systems (NeurIPS), 2021
Kate Donahue
Jon M. Kleinberg
FedML
116
61
0
17 Jun 2021
How Costly is Noise? Data and Disparities in Consumer Credit
How Costly is Noise? Data and Disparities in Consumer Credit
Laura Blattner
Scott Nelson
110
47
0
17 May 2021
Impact of Data Processing on Fairness in Supervised Learning
Impact of Data Processing on Fairness in Supervised LearningInternational Symposium on Information Theory (ISIT), 2021
S. Khodadadian
AmirEmad Ghassami
Negar Kiyavash
FaML
78
6
0
03 Feb 2021
Minimax Pareto Fairness: A Multi Objective Perspective
Minimax Pareto Fairness: A Multi Objective Perspective
Natalia Martínez
Martín Bertrán
Guillermo Sapiro
FaML
157
216
0
03 Nov 2020
Fairness Under Feature Exemptions: Counterfactual and Observational Measures
Fairness Under Feature Exemptions: Counterfactual and Observational MeasuresIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2020
Sanghamitra Dutta
Praveen Venkatesh
Piotr (Peter) Mardziel
Anupam Datta
P. Grover
158
17
0
14 Jun 2020
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic FairnessPattern Recognition (Pattern Recognit.), 2019
Zhu Li
Adrián Pérez-Suay
Gustau Camps-Valls
Dino Sejdinovic
FaML
126
23
0
11 Nov 2019

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