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Invariant learning methods, aimed at identifying a consistent predictor across multiple environments, are gaining prominence in out-of-distribution (OOD) generalization.
M. A. Hall et al. , “Correlation-based feature selection for machine learning,” 1999
1999
Earlier work this paper cites.
M. A. Hall, “Correlation-based feature selection of discrete and numeric class machine learning,” 2000
2000
Earlier work this paper cites.
L. Yu and H. Liu, “Feature selection for high-dimensional data: A fast correlation-based filter solution,” in International Conference on Machine Learning , 2003, pp. 856–863
2003
Earlier work this paper cites.
M. Sugiyama, M. Krauledat, and K.-R. Müller, “Covariate shift adaptation by importance weighted cross validation.” Journal of Machine Learning Research , vol. 8, no. 5, 2007
2007
Earlier work this paper cites.
M. Sugiyama, S. Nakajima, H. Kashima, P. Buenau, and M. Kawanabe, “Direct importance estimation with model selection and its application to covariate shift adaptation,” Advances in Neural Information Processing Systems , vol. 20, 2007
2007
Earlier work this paper cites.
M. Sugiyama, T. Suzuki, S. Nakajima, H. Kashima, P. Von Bünau, and M. Kawanabe, “Direct importance estimation for covariate shift adaptation,” Annals of the Institute of Statistical Mathematics , vol. 60, pp. 699–746, 2008
2008
Earlier work this paper cites.
S. Bickel, M. Brückner, and T. Scheffer, “Discriminative learning under covariate shift.” Journal of Machine Learning Research , vol. 10, no. 9, 2009
2009
Earlier work this paper cites.
J. F. P. d. Costa, Weighted Correlation . Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 1653–1655. [Online]. Available: https://doi.org/10.1007/978-3-642-04898-2_612
2011
Earlier work this paper cites.
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, “The caltech-ucsd birds-200-2011 dataset,” 2011
2011
Earlier work this paper cites.
E. C. Blessie and E. Karthikeyan, “Sigmis: a feature selection algorithm using correlation based method,” Journal of Algorithms & Computational Technology , vol. 6, no. 3, pp. 385–394, 2012
2012
Earlier work this paper cites.
M. Sugiyama and M. Kawanabe, Machine learning in non-stationary environments: Introduction to covariate shift adaptation . MIT Press, 2012
2012
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations , 2015
2015
Earlier work this paper cites.
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba, “Places: A 10 million image database for scene recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 40, no. 6, pp. 1452–1464, 2017
2017
Earlier work this paper cites.
S. Sagawa, P. W. Koh, T. B. Hashimoto, and P. Liang, “Distributionally robust neural networks,” in International Conference on Learning Representations , 2019
2019
Earlier work this paper cites.
R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A. Wichmann, “Shortcut learning in deep neural networks,” Nature Machine Intelligence , vol. 2, no. 11, pp. 665–673, 2020
2020
Cited alongside, same era.
M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz, “Invariant risk minimization,” Stat , vol. 1050, p. 27, 2020
2020
Cited alongside, same era.
E. Rosenfeld, P. K. Ravikumar, and A. Risteski, “The risks of invariant risk minimization,” in International Conference on Learning Representations , 2020
2020
Cited alongside, same era.
F. Ahmed, Y. Bengio, H. van Seijen, and A. Courville, “Systematic generalisation with group invariant predictions,” in International Conference on Learning Representations , 2020
2020
Cited alongside, same era.
K. Ahuja, K. Shanmugam, K. Varshney, and A. Dhurandhar, “Invariant risk minimization games,” in International Conference on Machine Learning . PMLR, 2020, pp. 145–155
K. Zhou, Z. Liu, Y. Qiao, T. Xiang, and C. C. Loy, “Domain generalization: A survey,” 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
E. Creager, J.-H. Jacobsen, and R. Zemel, “Environment inference for invariant learning,” in International Conference on Machine Learning . PMLR, 2021, pp. 2189–2200
2021
Later among the works it cites.
E. Z. Liu, B. Haghgoo, A. S. Chen, A. Raghunathan, P. W. Koh, S. Sagawa, P. Liang, and C. Finn, “Just train twice: Improving group robustness without training group information,” in International Conference on Machine Learning . PMLR, 2021, pp. 6781–6792
2021
Later among the works it cites.
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2020
Cited alongside, same era.
P. Chattopadhyay, Y. Balaji, and J. Hoffman, “Learning to balance specificity and invariance for in and out of domain generalization,” in European Conference on Computer Vision . Springer, 2020, pp. 301–318
2020
Cited alongside, same era.
N. Sohoni, J. Dunnmon, G. Angus, A. Gu, and C. Ré, “No subclass left behind: Fine-grained robustness in coarse-grained classification problems,” Advances in Neural Information Processing Systems , vol. 33, pp. 19 339–19 352, 2020
2020
Cited alongside, same era.
J. Nam, H. Cha, S. Ahn, J. Lee, and J. Shin, “Learning from failure: De-biasing classifier from biased classifier,” Advances in Neural Information Processing Systems , vol. 33, pp. 20 673–20 684, 2020
2020
Cited alongside, same era.
T. Matsuura and T. Harada, “Domain generalization using a mixture of multiple latent domains,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 11 749–11 756
2020
Cited alongside, same era.
Z. Shen, P. Cui, J. Liu, T. Zhang, B. Li, and Z. Chen, “Stable learning via differentiated variable decorrelation,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 2185–2193
2020
Cited alongside, same era.
K. Kuang, R. Xiong, P. Cui, S. Athey, and B. Li, “Stable prediction with model misspecification and agnostic distribution shift,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 04, 2020, pp. 4485–4492
2020
Cited alongside, same era.
T. Fang, N. Lu, G. Niu, and M. Sugiyama, “Rethinking importance weighting for deep learning under distribution shift,” Advances in Neural Information Processing Systems , vol. 33, pp. 11 996–12 007, 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
J. Liu, Z. Hu, P. Cui, B. Li, and Z. Shen, “Heterogeneous risk minimization,” in International Conference on Machine Learning . PMLR, 2021, pp. 6804–6814
2021
Later among the works it cites.
D. Krueger, E. Caballero, J.-H. Jacobsen, A. Zhang, J. Binas, D. Zhang, R. Le Priol, and A. Courville, “Out-of-distribution generalization via risk extrapolation (rex),” in International Conference on Machine Learning . PMLR, 2021, pp. 5815–5826
2021
Later among the works it cites.
X. Zhang, P. Cui, R. Xu, L. Zhou, Y. He, and Z. Shen, “Deep stable learning for out-of-distribution generalization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 5372–5382
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Quiñonero-Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence, Dataset shift in machine learning . MIT Press, 2022
2022
Closest in time.
N. Ye, K. Li, H. Bai, R. Yu, L. Hong, F. Zhou, Z. Li, and J. Zhu, “Ood-bench: Quantifying and understanding two dimensions of out-of-distribution generalization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 7947–7958
2022
Closest in time.
N. Dagaev, B. D. Roads, X. Luo, D. N. Barry, K. R. Patil, and B. C. Love, “A too-good-to-be-true prior to reduce shortcut reliance,” Pattern Recognition Letters , vol. 166, pp. 164–171, 2023
2023
Closest in time.
——, “Generalizing importance weighting to a universal solver for distribution shift problems,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.