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The invariance principle from causality is at the heart of notable approaches such as invariant risk minimization (IRM) that seek to address out-of-distribution (OOD) generalization failures.
On learning invariant representation for domain adaptation
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D. (2019) · 1907
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Adversarial target-invariant representation learning for domain generalization
Albuquerque, I., Monteiro, J., Falk, T. H., and Mitliagkas, I. (2019) · 1911
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Principles of risk minimization for learning theory
Vapnik, V. (1992) · 1992
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Causal diagrams for empirical research
Pearl, J. (1995) · 1995
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Probability and Measure Theory
Ash, R. B. and Doléans-Dade, C. A. (2000) · 2000
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The information bottleneck method
Tishby, N., Pereira, F. C., and Bialek, W. (2000) · 2000
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Unshuffling data for improved generalization
Teney, D., Abbasnejad, E., and Hengel, A. v. d. (2020) · 2002
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Unpacking information bottlenecks: Unifying information-theoretic objectives in deep learning
Kirsch, A., Lyle, C., and Gal, Y. (2020) · 2003
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Out-of-distribution generalization via risk extrapolation (rex)
Krueger, D., Caballero, E., Jacobsen, J.-H., Zhang, A., Binas, J., Zhang, D., Priol, R. L., and Courville, A. (2020) · 2003
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Deng, Z., Ding, F., Dwork, C., Hong, R., Parmigiani, G., Patil, P., and Sur, P. (2020) · 2006
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Domain generalization using causal matching
Mahajan, D., Tople, S., and Sharma, A. (2020) · 2006
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Analysis of representations for domain adaptation
Ben-David, S., Blitzer, J., Crammer, K., and Pereira, F. (2007) · 2007
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Out-of-distribution generalization with maximal invariant predictor
Koyama, M. and Yamaguchi, S. (2020) · 2008
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Lyapunov stability
Khalil, H. K. (2009) · 2009
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Causality
Pearl, J. (2009) · 2009
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A theory of learning from different domains
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Vaughan, J. W. (2010) · 2010
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Impossibility theorems for domain adaptation
David, S. B., Lu, T., Luu, T., and Pál, D. (2010) · 2010
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Learning robust models using the principle of independent causal mechanisms
Müller, J., Schmier, R., Ardizzone, L., Rother, C., and Köthe, U. (2020) · 2010
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Gradient starvation: A learning proclivity in neural networks
Pezeshki, M., Kaba, S.-O., Bengio, Y., Courville, A., Precup, D., and Lajoie, G. (2020) · 2011
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On the hardness of domain adaptation and the utility of unlabeled target samples
Ben-David, S. and Urner, R. (2012) · 2012
Cited alongside, same era.
On causal and anticausal learning
Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., and Mooij, J. (2012) · 2012
Cited alongside, same era.
Domain generalization via invariant feature representation
Muandet, K., Balduzzi, D., and Schölkopf, B. (2013) · 2013
Cited alongside, same era.
Causal inference using invariant prediction: identification and confidence intervals
Peters, J., Bühlmann, P., and Meinshausen, N. (2015) · 2015
Cited alongside, same era.
Deep variational information bottleneck
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K. (2016) · 2016
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AI for radiographic COVID-19 detection selects shortcuts over signal
DeGrave, A. J., Janizek, J. D., and Lee, S.-I. (2020) · 2020
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Shortcut learning in deep neural networks
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., and Wichmann, F. A. (2020) · 2020
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Robust learning with the hilbert-schmidt independence criterion
Greenfeld, D. and Shalit, U. (2020) · 2020
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Enforcing predictive invariance across structured biomedical domains
Jin, W., Barzilay, R., and Jaakkola, T. (2020) · 2020
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Variational autoencoders and nonlinear ica: A unifying framework
Khemakhem, I., Kingma, D., Monti, R., and Hyvarinen, A. (2020) · 2020
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Domain generalization using a mixture of multiple latent domains
Matsuura, T. and Harada, T. (2020) · 2020
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Causal inference by using invariant prediction: identification and confidence intervals
Peters, J., Bühlmann, P., and Meinshausen, N. (2016) · 2016
Cited alongside, same era.
Differential equations with applications and historical notes
Simmons, G. F. (2016) · 2016
Cited alongside, same era.
The deterministic information bottleneck
Strouse, D. and Schwab, D. J. (2017) · 2017
Cited alongside, same era.
Recognition in terra incognita
Beery, S., Van Horn, G., and Perona, P. (2018) · 2018
Cited alongside, same era.
Invariant causal prediction for nonlinear models
Heinze-Deml, C., Peters, J., and Meinshausen, N. (2018) · 2018
Cited alongside, same era.
Deep domain generalization via conditional invariant adversarial networks
Li, Y., Tian, X., Gong, M., Liu, Y., Liu, T., Zhang, K., and Tao, D. (2018) · 2018
Cited alongside, same era.
Pac learning guarantees under covariate shift
Pagnoni, A., Gramatovici, S., and Liu, S. (2018) · 2018
Cited alongside, same era.
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Efficient domain generalization via common-specific low-rank decomposition
Piratla, V., Netrapalli, P., and Sarawagi, S. (2020) · 2020
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Domain generalization via entropy regularization
Zhao, S., Gong, M., Liu, T., Fu, H., and Tao, D. (2020) · 2020
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Systematic generalisation with group invariant predictions
Ahmed, F., Bengio, Y., van Seijen, H., and Courville, A. (2021) · 2021
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Linear unit-tests for invariance discovery
Aubin, B., Słowik, A., Arjovsky, M., Bottou, L., and Lopez-Paz, D. (2021) · 2021
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Learn to expect the unexpected: Probably approximately correct domain generalization
Garg, V., Kalai, A. T., Ligett, K., and Wu, S. (2021) · 2021
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In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D. (2021) · 2021
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Does invariant risk minimization capture invariance?
Kamath, P., Tangella, A., Sutherland, D. J., and Srebro, N. (2021) · 2021
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Nonlinear invariant risk minimization: A causal approach
Lu, C., Wu, Y., Hernández-Lobato, J. M., and Schölkopf, B. (2021) · 2021
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Understanding the failure modes of out-of-distribution generalization
Nagarajan, V., Andreassen, A., and Neyshabur, B. (2021) · 2021
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Learning explanations that are hard to vary
Parascandolo, G., Neitz, A., ORVIETO, A., Gresele, L., and Schölkopf, B. (2021) · 2021
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Model-based domain generalization
Robey, A., Pappas, G. J., and Hassani, H. (2021) · 2021
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In-n-out: Pre-training and self-training using auxiliary information for out-of-distribution robustness
Xie, S. M., Kumar, A., Jones, R., Khani, F., Ma, T., and Liang, P. (2021) · 2021
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Can subnetwork structure be the key to out-of-distribution generalization?
Zhang, D., Ahuja, K., Xu, Y., Wang, Y., and Courville, A. C. (2021) · 2021
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V. (2016) · 2030
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