Fetching the paper…
Reading the bibliography…
As machine learning has been deployed ubiquitously across applications in modern data science, algorithmic fairness has become a great concern.
Roc graphs: Notes and practical considerations for researchers
Fawcett, T · 2004
Earlier work this paper cites.
A firm foundation for private data analysis
Dwork, C · 2011
Earlier work this paper cites.
Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
Earlier work this paper cites.
Data preprocessing techniques for classification without discrimination
Kamiran, F. and Calders, T · 2012
Earlier work this paper cites.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Earlier work this paper cites.
Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
Earlier work this paper cites.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A · 2017
Earlier work this paper cites.
Fairness constraints: Mechanisms for fair classification
Zafar, M. B., Valera, I., Rogriguez, M. G., and Gummadi, K. P · 2017
Earlier work this paper cites.
Age progression/regression by conditional adversarial autoencoder
Zhang, Z., Song, Y., and Qi, H · 2017
Earlier work this paper cites.
Analysis of a degenerate parabolic cross-diffusion system for ion transport
Gerstenmayer, A. and Jüngel, A · 2018
Cited alongside, same era.
Learning adversarially fair and transferable representations
Madras, D., Creager, E., Pitassi, T., and Zemel, R · 2018
Cited alongside, same era.
Fnnc: achieving fairness through neural networks
Manisha, P. and Gujar, S · 2018
Cited alongside, same era.
Multiaccuracy: Black-box post-processing for fairness in classification
Kim, M. P., Ghorbani, A., and Zou, J · 2019
Cited alongside, same era.
On the apparent conflict between individual and group fairness
Binns, R · 2020
Cited alongside, same era.
Representation via representations: Domain generalization via adversarially learned invariant representations
How does mixup help with robustness and generalization?
Zhang, L., Deng, Z., Kawaguchi, K., Ghorbani, A., and Zou, J · 2020
Later among the works it cites.
Burhanpurkar, M., Deng, Z., Dwork, C., and Zhang, L · 2021
Later among the works it cites.
Technical challenges for training fair neural networks
Cherepanova, V., Nanda, V., Goldblum, M., Dickerson, J. P., and Goldstein, T · 2021
Later among the works it cites.
Retiring adult: New datasets for fair machine learning
Ding, F., Hardt, M., Miller, J., and Schmidt, L · 2021
Later among the works it cites.
Post-processing for individual fairness
Petersen, F., Mukherjee, D., Sun, Y., and Yurochkin, M · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deng, Z., Ding, F., Dwork, C., Hong, R., Parmigiani, G., Patil, P., and Sur, P · 2020
Cited alongside, same era.
Fairness without demographics through adversarially reweighted learning
Lahoti, P., Beutel, A., Chen, J., Lee, K., Prost, F., Thain, N., Wang, X., and Chi, E · 2020
Cited alongside, same era.
Maskgan: Towards diverse and interactive facial image manipulation
Lee, C.-H., Liu, Z., Wu, L., and Luo, P · 2020
Cited alongside, same era.
Readme: Representation learning by fairness-aware disentangling method, 2020
Park, S., Kim, D., Hwang, S., and Byun, H · 2020
Cited alongside, same era.
Toward better generalization bounds with locally elastic stability
Deng, Z., He, H., and Su, W
Cited in the paper.
Improving adversarial robustness via unlabeled out-of-domain data
Deng, Z., Zhang, L., Ghorbani, A., and Zou, J
Cited in the paper.
Adversarial training helps transfer learning via better representations
Deng, Z., Zhang, L., Vodrahalli, K., Kawaguchi, K., and Zou, J. Y
Cited in the paper.
Kirichenko, P., Izmailov, P., and Wilson, A. G · 2022
Later among the works it cites.
Surgical fine-tuning improves adaptation to distribution shifts
Lee, Y., Chen, A. S., Tajwar, F., Kumar, A., Yao, H., Liang, P., and Finn, C · 2022
Later among the works it cites.
How does information bottleneck help deep learning?
Kawaguchi, K., Deng, Z., Ji, X., and Huang, J · 2023
Closest in time.