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Invariant risk minimization (IRM) (Arjovsky et al., 2019) is a recently proposed framework designed for learning predictors that are invariant to spurious correlations across different training environments.
Unbiased look at dataset bias
Torralba, A. and Efros, A. A · 2011
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., and Potts, C · 2013
Earlier work this paper cites.
Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C · 2014
Earlier work this paper cites.
Hyperopt: a python library for model selection and hyperparameter optimization
Bergstra, J., Komer, B., Eliasmith, C., Yamins, D., and Cox, D. D · 2015
Earlier work this paper cites.
Causal inference by using invariant prediction: identification and confidence intervals
Peters, J., Bühlmann, P., and Meinshausen, N · 2016
Earlier work this paper cites.
Annotation artifacts in natural language inference data
Gururangan, S., Swayamdipta, S., Levy, O., Schwartz, R., Bowman, S., and Smith, N. A · 2017
Cited alongside, same era.
Recognition in terra incognita
Beery, S., Van Horn, G., and Perona, P · 2018
Cited alongside, same era.
Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
Cited alongside, same era.
Are we modeling the task or the annotator? an investigation of annotator bias in natural language understanding datasets
Geva, M., Goldberg, Y., and Berant, J · 2019
Cited alongside, same era.
Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
McCoy, T., Pavlick, E., and Linzen, T · 2019
Cited alongside, same era.
Probing neural network comprehension of natural language arguments
Niven, T. and Kao, H.-Y · 2019
Later among the works it cites.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2019
Later among the works it cites.
Invariant risk minimization games
Ahuja, K., Shanmugam, K., Varshney, K., and Dhurandhar, A · 2020
Closest in time.
Unshuffling data for improved generalization
Teney, D., Abbasnejad, E., and Hengel, A. v. d · 2020
Closest in time.
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