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Traditional machine learning paradigms are based on the assumption that both training and test data follow the same statistical pattern, which is mathematically referred to as Independent and Identically Distributed ($i.i.d.$).
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Large-scale methods for distributionally robust optimization
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Open graph benchmark: Datasets for machine learning on graphs
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Metareg: Towards domain generalization using meta-regularization
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From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge
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Data-driven robust optimization
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Invariance, causality and robustness
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A robust learning approach for regression models based on distributionally robust optimization
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Functional map of the world
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Batch normalization embeddings for deep domain generalization
M. Segu, A. Tonioni, and F. Tombari · 2020
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The pitfalls of simplicity bias in neural networks
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Disentangled generative causal representation learning
X. Shen, F. Liu, H. Dong, Q. Lian, Z. Chen, and T. Zhang · 2020
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Decorrelated clustering with data selection bias
X. Wang, S. Fan, K. Kuang, C. Shi, J. Liu, and B. Wang · 2020
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Unseen target stance detection with adversarial domain generalization
Z. Wang, Q. Wang, C. Lv, X. Cao, and G. Fu · 2020
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Using publicly available satellite imagery and deep learning to understand economic well-being in africa
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Devlbert: Learning deconfounded visio-linguistic representations
S. Zhang, T. Jiang, T. Wang, K. Kuang, Z. Zhao, J. Zhu, J. Yu, H. Yang, and F. Wu · 2020
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