Fetching the paper…
Reading the bibliography…
Algorithmic fairness plays an important role in machine learning and imposing fairness constraints during learning is a common approach.
The Dutch virtual census of 2001, 2001
C. B. voor de Statistiek (Statistics Netherlands) and M. P. Center · 2001
Earlier work this paper cites.
The perceptron algorithm with uneven margins
Y. Li, H. Zaragoza, R. Herbrich, J. Shawe-Taylor, and J. Kandola · 2002
Earlier work this paper cites.
kNN approach to unbalanced data distributions: a case study involving information extraction
I. Mani and I. Zhang · 2003
Earlier work this paper cites.
Representation via representations: Domain generalization via adversarially learned invariant representations
Z. Deng, F. Ding, C. Dwork, R. Hong, G. Parmigiani, P. Patil, and P. Sur · 2006
Earlier work this paper cites.
On the complexity of linear prediction: Risk bounds, margin bounds, and regularization
S. M. Kakade, K. Sridharan, and A. Tewari · 2008
Earlier work this paper cites.
Learning from imbalanced data
H. He and E. A. Garcia · 2009
Earlier work this paper cites.
Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
Earlier work this paper cites.
Imbalanced learning: foundations, algorithms, and applications
H. He and Y. Ma · 2013
Earlier work this paper cites.
Learning fair representations
R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
Earlier work this paper cites.
Censoring representations with an adversary
H. Edwards and A. Storkey · 2015
Earlier work this paper cites.
Certifying and removing disparate impact
M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
Earlier work this paper cites.
Big data’s disparate impact
S. Barocas and A. D. Selbst · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
Earlier work this paper cites.
Learning from imbalanced data: open challenges and future directions
B. Krawczyk · 2016
Earlier work this paper cites.
Large-margin softmax loss for convolutional neural networks
W. Liu, Y. Wen, Z. Yu, and M. Yang · 2016
Earlier work this paper cites.
Active bias: Training more accurate neural networks by emphasizing high variance samples
H.-S. Chang, E. Learned-Miller, and A. McCallum · 2017
Earlier work this paper cites.
UCI machine learning repository, 2017
D. Dua and C. Graff · 2017
Earlier work this paper cites.
Learning from class-imbalanced data: Review of methods and applications
G. Haixiang, L. Yijing, J. Shang, G. Mingyun, H. Yuanyue, and G. Bing · 2017
Earlier work this paper cites.
Learning to model the tail
Y.-X. Wang, D. Ramanan, and M. Hebert · 2017
Cited alongside, same era.
Fairness constraints: Mechanisms for fair classification
M. B. Zafar, I. Valera, M. G. Rogriguez, and K. P. Gummadi · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2017
Cited alongside, same era.
A reductions approach to fair classification
A. Agarwal, A. Beygelzimer, M. Dudík, J. Langford, and H. Wallach · 2018
Cited alongside, same era.
The measure and mismeasure of fairness: A critical review of fair machine learning
S. Corbett-Davies and S. Goel · 2018
Cited alongside, same era.
Empirical risk minimization under fairness constraints
M. Donini, L. Oneto, S. Ben-David, J. S. Shawe-Taylor, and M. Pontil · 2018
Cited alongside, same era.
Experiment tracking with weights and biases, 2020
L. Biewald · 2020
Later among the works it cites.
Fairlearn: A toolkit for assessing and improving fairness in AI
S. Bird, M. Dudík, R. Edgar, B. Horn, R. Lutz, V. Milan, M. Sameki, H. Wallach, and K. Walker · 2020
Later among the works it cites.
Fairness without demographics through adversarially reweighted learning
P. Lahoti, A. Beutel, J. Chen, K. Lee, F. Prost, N. Thain, X. Wang, and E. Chi · 2020
Later among the works it cites.
Co-designing checklists to understand organizational challenges and opportunities around fairness in ai
M. A. Madaio, L. Stark, J. Wortman Vaughan, and H. Wallach · 2020
Later among the works it cites.
Minimax Pareto fairness: A multi objective perspective
N. Martinez, M. Bertran, and G. Sapiro · 2020
Later among the works it cites.
Deep double descent: Where bigger models and more data hurt
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning adversarially fair and transferable representations
D. Madras, E. Creager, T. Pitassi, and R. Zemel · 2018
Cited alongside, same era.
Adversarially robust generalization requires more data
L. Schmidt, S. Santurkar, D. Tsipras, K. Talwar, and A. Madry · 2018
Cited alongside, same era.
Putting fairness principles into practice: Challenges, metrics, and improvements
A. Beutel, J. Chen, T. Doshi, H. Qian, A. Woodruff, C. Luu, P. Kreitmann, J. Bischof, and E. H. Chi · 2019
Cited alongside, same era.
What is the effect of importance weighting in deep learning?
J. Byrd and Z. Lipton · 2019
Cited alongside, same era.
Learning imbalanced datasets with label-distribution-aware margin loss
K. Cao, C. Wei, A. Gaidon, N. Arechiga, and T. Ma · 2019
Cited alongside, same era.
Training well-generalizing classifiers for fairness metrics and other data-dependent constraints
A. Cotter, M. Gupta, H. Jiang, N. Srebro, K. Sridharan, S. Wang, B. Woodworth, and S. You · 2019
Cited alongside, same era.
P. Nakkiran, G. Kaplun, Y. Bansal, T. Yang, B. Barak, and I. Sutskever · 2020
Later among the works it cites.
An investigation of why overparameterization exacerbates spurious correlations
S. Sagawa, A. Raghunathan, P. W. Koh, and P. Liang · 2020
Later among the works it cites.
Measuring non-expert comprehension of machine learning fairness metrics
D. Saha, C. Schumann, D. Mcelfresh, J. Dickerson, M. Mazurek, and M. Tschantz · 2020
Later among the works it cites.
How does mixup help with robustness and generalization?
L. Zhang, Z. Deng, K. Kawaguchi, A. Ghorbani, and J. Zou · 2020
Later among the works it cites.
Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients
J. An, L. Ying, and Y. Zhu · 2021
Later among the works it cites.
Scaffolding sets
M. Burhanpurkar, Z. Deng, C. Dwork, and L. Zhang · 2021
Later among the works it cites.
Technical challenges for training fair neural networks
V. Cherepanova, V. Nanda, M. Goldblum, J. P. Dickerson, and T. Goldstein · 2021
Later among the works it cites.
Racial/ethnic disparities in the performance of prediction models for death by suicide after mental health visits
R. Y. Coley, E. Johnson, G. E. Simon, M. Cruz, and S. M. Shortreed · 2021
Later among the works it cites.
How shrinking gradient noise helps the performance of neural networks
Z. Deng, J. Huang, and K. Kawaguchi · 2021
Later among the works it cites.
Label-imbalanced and group-sensitive classification under overparameterization
G. R. Kini, O. Paraskevas, S. Oymak, and C. Thrampoulidis · 2021
Later among the works it cites.
A survey on bias and fairness in machine learning
N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan · 2021
Later among the works it cites.
When and how mixup improves calibration
L. Zhang, Z. Deng, K. Kawaguchi, and J. Y. Zou · 2021
Later among the works it cites.
Understanding dynamics of nonlinear representation learning and its application
K. Kawaguchi, L. Zhang, and Z. Deng · 2022
Closest in time.