
Metalearning Transferable Representations with a Single Target Domain
Recent works found that finetuning and joint training—two popular appro...
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Theoretical Analysis of SelfTraining with Deep Networks on Unlabeled Data
Selftraining algorithms, which train a model to fit pseudolabels predic...
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Selftraining Avoids Using Spurious Features Under Domain Shift
In unsupervised domain adaptation, existing theory focuses on situations...
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Shape Matters: Understanding the Implicit Bias of the Noise Covariance
The noise in stochastic gradient descent (SGD) provides a crucial implic...
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The Implicit and Explicit Regularization Effects of Dropout
Dropout is a widelyused regularization technique, often required to obt...
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Improved Sample Complexities for Deep Networks and Robust Classification via an AllLayer Margin
For linear classifiers, the relationship between (normalized) output mar...
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Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks
Stochastic gradient descent with a large initial learning rate is a wide...
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Learning Imbalanced Datasets with LabelDistributionAware Margin Loss
Deep learning algorithms can fare poorly when the training dataset suffe...
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Datadependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation
Existing Rademacher complexity bounds for neural networks rely only on n...
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On the Margin Theory of Feedforward Neural Networks
Past works have shown that, somewhat surprisingly, overparametrization ...
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Markov Chain Truncation for DoublyIntractable Inference
Computing partition functions, the normalizing constants of probability ...
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Colin Wei
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