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Domain generalization (DG) seeks predictors which perform well on unseen test distributions by leveraging data drawn from multiple related training distributions or domains.
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Domain generalization via invariant feature representation
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How image degradations affect deep cnn-based face recognition?
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"why should i trust you?": Explaining the predictions of any classifier
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Causal inference by using invariant prediction: identification and confidence intervals
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Domain-adversarial training of neural networks
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Domain adaptation with conditional transferable components
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Deep residual learning for image recognition
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The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study
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Causality matters in medical imaging
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In search of lost domain generalization
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Domain generalization via entropy regularization
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Towards deep learning models resistant to adversarial attacks
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Risk-constrained reinforcement learning with percentile risk criteria
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Behind distribution shift: Mining driving forces of changes and causal arrows
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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales · 2017
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Deep hashing network for unsupervised domain adaptation
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Adaptive sampling for stochastic risk-averse learning
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Learning bounds for risk-sensitive learning
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Uniform convergence of rank-weighted learning
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Active invariant causal prediction: Experiment selection through stability
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Out-of-distribution generalization with maximal invariant predictor
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Open graph benchmark: Datasets for machine learning on graphs
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Diva: Domain invariant variational autoencoders
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Highly accurate protein structure prediction with alphafold
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WILDS: A benchmark of in-the-wild distribution shifts
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Do image classifiers generalize across time?
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Source-free adaptation to measurement shift via bottom-up feature restoration
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Detect and correct bias in multi-site neuroimaging datasets
Christian Wachinger, Anna Rieckmann, Sebastian Pölsterl, Alzheimer’s Disease Neuroimaging Initiative, et al · 2021
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Out-of-distribution generalization via risk extrapolation (REx)
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Domain generalization by marginal transfer learning
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Adaptive risk minimization: Learning to adapt to domain shift
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Understanding the failure modes of out-of-distribution generalization
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The iWildCam 2021 competition dataset
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Invariance principle meets information bottleneck for out-of-distribution generalization
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A causal framework for distribution generalization
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Optimization-based causal estimation from heterogenous environments
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Adversarially robust kernel smoothing
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Blind pareto fairness and subgroup robustness
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Minimax group fairness: Algorithms and experiments
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Open domain generalization with domain-augmented meta-learning
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Adaptive methods for real-world domain generalization
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Selfreg: Self-supervised contrastive regularization for domain generalization
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Stl robustness risk over discrete-time stochastic processes
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Lectures on stochastic programming: modeling and theory
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