A repository of resources for Representation Learning as applicable to invariance, fairness or information leakage.

Papers

Other Code Repositories

References

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  1. Zemel, R., Wu, Y., Swersky, K., Pitassi, T., & Dwork, C. (2013). Learning fair representations. In International Conference on Machine Learning (ICML).
  2. Edwards, H., & Storkey, A. (2015). Censoring representations with an adversary. ArXiv:1511.05897.
  3. Louizos, C., Swersky, K., Li, Y., Welling, M., & Zemel, R. (2015). The variational fair autoencoder. ArXiv:1511.00830.
  4. Xie, Q., Dai, Z., Du, Y., Hovy, E., & Neubig, G. (2017). Controllable invariance through adversarial feature learning. In Advances in Neural Information Processing Systems (NeurIPS).
  5. Pérez-Suay, A., Laparra, V., Mateo-Garcı́a Gonzalo, Muñoz-Marı́ Jordi, Gómez-Chova, L., & Camps-Valls, G. (2017). Fair kernel learning. In European Conference on Machine Learning and Knowledge Discovery in Databases (ECMLPKDD).
  6. Madras, D., Creager, E., Pitassi, T., & Zemel, R. (2018). Learning adversarially fair and transferable representations. ArXiv:1802.06309.
  7. Moyer, D., Gao, S., Brekelmans, R., Galstyan, A., & Ver Steeg, G. (2018). Invariant Representations without Adversarial Training. In Advances in Neural Information Processing Systems (NeurIPS).
  8. Madras, D., Pitassi, T., & Zemel, R. (2018). Predict responsibly: improving fairness and accuracy by learning to defer. In Advances in Neural Information Processing Systems (NeurIPS).
  9. Elazar, Y., & Goldberg, Y. (2018). Adversarial removal of demographic attributes from text data. In Empirical Methods in Natural Language Processing (EMNLP).
  10. Zhang, B. H., Lemoine, B., & Mitchell, M. (2018). Mitigating unwanted biases with adversarial learning. In Conference on Artificial Intelligence, Ethics and Soceity (AIES).
  11. Roy, P., & Boddeti, V. (2019). Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  12. Song, J., Kalluri, P., Grover, A., Zhao, S., & Ermon, S. (2019). Learning Controllable Fair Representations. In International Conference on Artificial Intelligence and Statistics (AISTATS).
  13. Sadeghi, B., Yu, R., & Boddeti, V. (2019). On the Global Optima of Kernelized Adversarial Representation Learning. In International Conference on Computer Vision (ICCV).
  14. Bertran, M., Martinez, N., Papadaki, A., Qiu, Q., Rodrigues, M., Reeves, G., & Sapiro, G. (2019). Adversarially Learned Representations for Information Obfuscation and Inference. In International Conference on Machine Learning (ICML).
  15. Tan, Z., Yeom, S., Fredrikson, M., & Talwalkar, A. (2019). Learning Fair Representations for Kernel Models. ArXiv:1906.11813.
  16. Creager, E., Madras, D., Jacobsen, J.-H., Weis, M. A., Swersky, K., Pitassi, T., & Zemel, R. (2019). Flexibly Fair Representation Learning by Disentanglement. In International Conference on Machine Learning (ICML).
  17. Kim, B., Kim, H., Kim, K., Kim, S., & Kim, J. (2019). Learning Not to Learn: Training Deep Neural Networks with Biased Data. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  18. Sarafianos, N., Xu, X., & Kakadiaris, I. A. (2019). Adversarial Representation Learning for Text-to-Image Matching. In International Conference on Computer Vision (ICCV).
  19. Moyer, D., Steeg, G. V., Tax, C. M. W., & Thompson, P. M. (2019). Scanner Invariant Representations for Diffusion MRI Harmonization. ArXiv Preprint ArXiv:1904.05375.
  20. Jaiswal, A., Moyer, D., Steeg, G. V., AbdAlmageed, W., & Natarajan, P. (2020). Invariant Representations through Adversarial Forgetting. In AAAI Conference on Artificial Intelligence (AAAI).