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We propose Predict then Interpolate (PI), a simple algorithm for learning correlations that are stable across environments.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Training products of experts by minimizing contrastive divergence
Hinton, G. E · 2002
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Making things happen: A theory of causal explanation
Woodward, J · 2005
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Learning attitudes and attributes from multi-aspect reviews
McAuley, J., Leskovec, J., and Jurafsky, D · 2012
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Robust solutions of optimization problems affected by uncertain probabilities
Ben-Tal, A., Den Hertog, D., De Waegenaere, A., Melenberg, B., and Rennen, G · 2013
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Convolutional neural networks for sentence classification
Kim, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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A large annotated corpus for learning natural language inference
Bowman, S. R., Angeli, G., Potts, C., and Manning, C. D · 2015
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Causal inference using invariant prediction: identification and confidence intervals
Peters, J., Bühlmann, P., and Meinshausen, N · 2015
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Rationalizing neural predictions
Lei, T., Barzilay, R., and Jaakkola, T · 2016
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Causal inference by using invariant prediction: identification and confidence intervals
Peters, J., Bühlmann, P., and Meinshausen, N · 2016
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Deriving machine attention from human rationales
Bao, Y., Chang, S., Yu, M., and Barzilay, R · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J. and Gebru, T · 2018
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Learning models with uniform performance via distributionally robust optimization
Duchi, J. and Namkoong, H · 2018
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Does distributionally robust supervised learning give robust classifiers?
Hu, W., Niu, G., Sato, I., and Sugiyama, M · 2018
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Large-scale comparison of machine learning methods for drug target prediction on chembl
Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., and Hochreiter, S · 2018
Cited alongside, same era.
Advances in pre-training distributed word representations
Mikolov, T., Grave, E., Bojanowski, P., Puhrsch, C., and Joulin, A · 2018
Cited alongside, same era.
Moleculenet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., and Pande, V · 2018
Cited alongside, same era.
Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
Cited alongside, same era.
Using machine learning and natural language processing to review and classify the medical literature on cancer susceptibility genes
Bao, Y., Deng, Z., Wang, Y., Kim, H., Armengol, V. D., Acevedo, F., Ouardaoui, N., Wang, C., Parmigiani, G., Barzilay, R., Braun, D., and Hughes, K. S · 2019
HellaSwag: Can a machine really finish your sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y · 2019
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Invariant risk minimization games
Ahuja, K., Shanmugam, K., Varshney, K., and Dhurandhar, A · 2020
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Chang, S., Zhang, Y., Yu, M., and Jaakkola, T. S · 2020
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An empirical study of invariant risk minimization
Choe, Y. J., Ham, J., and Park, K · 2020
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In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D · 2020
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Cited alongside, same era.
Don’t take the premise for granted: Mitigating artifacts in natural language inference
Belinkov, Y., Poliak, A., Shieber, S. M., Van Durme, B., and Rush, A. M · 2019
Cited alongside, same era.
Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Clark, C., Yatskar, M., and Zettlemoyer, L · 2019
Cited alongside, same era.
Validation of a semiautomated natural language processing–based procedure for meta-analysis of cancer susceptibility gene penetrance
Deng, Z., Yin, K., Bao, Y., Armengol, V. D., Wang, C., Tiwari, A., Barzilay, R., Parmigiani, G., Braun, D., and Hughes, K. S · 2019
Cited alongside, same era.
Unlearn dataset bias in natural language inference by fitting the residual
He, H., Zha, S., and Wang, H · 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.
Distributionally robust language modeling
Oren, Y., Sagawa, S., Hashimoto, T., and Liang, P · 2019
Cited alongside, same era.
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2019
Cited alongside, same era.
Enforcing predictive invariance across structured biomedical domains, 2020
Jin, W., Barzilay, R., and Jaakkola, T · 2020
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Out-of-distribution generalization via risk extrapolation (rex), 2020
Krueger, D., Caballero, E., Jacobsen, J.-H., Zhang, A., Binas, J., Zhang, D., Priol, R. L., and Courville, A · 2020
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End-to-end bias mitigation by modelling biases in corpora
Mahabadi, R. K., Belinkov, Y., and Henderson, J · 2020
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Distributionally robust neural networks
Sagawa*, S., Koh*, P. W., Hashimoto, T. B., and Liang, P · 2020
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An investigation of why overparameterization exacerbates spurious correlations
Sagawa, S., Raghunathan, A., Koh, P. W., and Liang, P · 2020
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Winogrande: An adversarial winograd schema challenge at scale
Sakaguchi, K., Le Bras, R., Bhagavatula, C., and Choi, Y · 2020
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Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training
Stacey, J., Minervini, P., Dubossarsky, H., Riedel, S., and Rocktäschel, T · 2020
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Towards debiasing nlu models from unknown biases
Utama, P. A., Moosavi, N. S., and Gurevych, I · 2020
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Learning from others’ mistakes: Avoiding dataset biases without modeling them
Sanh, V., Wolf, T., Belinkov, Y., and Rush, A. M · 2021
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