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The ability to generalize out-of-domain (OOD) is an important goal for deep neural network development, and researchers have proposed many high-performing OOD generalization methods from various foundations.
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A Survey on Bias and Fairness in Machine Learning
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Learning the difference that makes a difference with counterfactually-augmented data
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Reducing Domain Gap by Reducing Style Bias
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Improve Unsupervised Domain Adaptation with Mixup Training
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Can neural networks acquire a structural bias from raw linguistic data?
Warstadt, A. and Bowman, S. R · 2007
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When is invariance useful in an Out-of-Distribution Generalization problem?
Koyama, M. and Yamaguchi, S · 2008
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Learning explanations that are hard to vary
Parascandolo, G., Neitz, A., Orvieto, A., Gresele, L., and Schölkopf, B · 2009
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Gradient Starvation: A Learning Proclivity in Neural Networks
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Unbiased Metric Learning: On the Utilization of Multiple Datasets and Web Images for Softening Bias
Fang, C., Xu, Y., and Rockmore, D. N · 2013
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Learning fair representations
Zemel, R., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C · 2013
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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 · 2015
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Domain Generalization for Object Recognition with Multi-task Autoencoders
Ghifary, M., Kleijn, W. B., Zhang, M., and Balduzzi, D · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Deep CORAL: Correlation Alignment for Deep Domain Adaptation
Sun, B. and Saenko, K · 2016
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Understanding intermediate layers using linear classifier probes
Alain, G. and Bengio, Y · 2017
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SentEval: An Evaluation Toolkit for Universal Sentence Representations
Conneau, A. and Kiela, D · 2018
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What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
Conneau, A., Kruszewski, G., Lample, G., Barrault, L., and Baroni, M · 2018
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Assessing Composition in Sentence Vector Representations
Ettinger, A., Elgohary, A., Phillips, C., and Resnik, P · 2018
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Domain Generalization with Adversarial Feature Learning
Li, H., Pan, S. J., Wang, S., and Kot, A. C · 2018
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Delayed Impact of Fair Machine Learning
Liu, L. T., Dean, S., Rolf, E., Simchowitz, M., and Hardt, M · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Designing and Interpreting Probes with Control Tasks
Hewitt, J. and Liang, P · 2019
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Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., Song, D., Steinhardt, J., and Gilmer, J · 2021
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Conditional probing: measuring usable information beyond a baseline
Hewitt, J., Ethayarajh, K., Liang, P., and Manning, C · 2021
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Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach
Hou, Y. and Sachan, M · 2021
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Removing spurious features can hurt accuracy and affect groups disproportionately
Khani, F. and Liang, P · 2021
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SelfReg: Self-supervised Contrastive Regularization for Domain Generalization
Kim, D., Park, S., Kim, J., and Lee, J · 2021
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McCoy, T., Pavlick, E., and Linzen, T · 2019
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Understanding the limitations of variational mutual information estimators
Song, J. and Ermon, S · 2019
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BERT Rediscovers the Classical NLP Pipeline
Tenney, I., Das, D., and Pavlick, E · 2019
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In Search of Lost Domain Generalization
Gulrajani, I. and Lopez-Paz, D · 2020
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Self-Challenging Improves Cross-Domain Generalization
Huang, Z., Wang, H., Xing, E. P., and Huang, D · 2020
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Emergent linguistic structure in artificial neural networks trained by self-supervision
Manning, C. D., Clark, K., Hewitt, J., Khandelwal, U., and Levy, O · 2020
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Formal limitations on the measurement of mutual information
McAllester, D. and Stratos, K · 2020
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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 · 2021
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Predicting Inductive Biases of Pre-Trained Models
Lovering, C., Jha, R., Linzen, T., and Pavlick, E · 2021
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Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?
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Optimal Representations for Covariate Shift
Ruan, Y., Dubois, Y., and Maddison, C. J · 2021
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Shahtalebi, S., Gagnon-Audet, J.-C., Laleh, T., Faramarzi, M., Ahuja, K., and Rish, I · 2021
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Gradient Matching for Domain Generalization
Shi, Y., Seely, J., Torr, P. H. S., Siddharth, N., Hannun, A., Usunier, N., and Synnaeve, G · 2021
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On Calibration and Out-of-Domain Generalization
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Learning Representations that Support Robust Transfer of Predictors
Xu, Y. and Jaakkola, T · 2021
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OoD-Bench: Benchmarking and understanding out-of-distribution generalization datasets and algorithms
Ye, N., Li, K., Hong, L., Bai, H., Chen, Y., Zhou, F., and Li, Z · 2021
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The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization
Huang, Z., Wang, H., Huang, D., Lee, Y. J., and Xing, E. P · 2022
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Semantic Structure in Deep Learning
Pavlick, E · 2022
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The VoicePrivacy 2020 Challenge: Results and findings
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On the data requirements of probing
Zhu, Z., Wang, J., Li, B., and Rudzicz, F · 2022
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