
A Theory of Label Propagation for Subpopulation Shift
One of the central problems in machine learning is domain adaptation. Un...
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Towards Certifying ℓ_∞ Robustness using Neural Networks with ℓ_∞dist Neurons
It is wellknown that standard neural networks, even with a high classif...
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SanityChecking Pruning Methods: Random Tickets can Win the Jackpot
Network pruning is a method for reducing testtime computational resourc...
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GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
Normalization plays an important role in the optimization of deep neural...
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RANDOM MASK: Towards Robust Convolutional Neural Networks
Robustness of neural networks has recently been highlighted by the adver...
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Locally Differentially Private (Contextual) Bandits Learning
We study locally differentially private (LDP) bandits learning in this p...
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Defective Convolutional Layers Learn Robust CNNs
Robustness of convolutional neural networks has recently been highlighte...
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Convergence of Adversarial Training in Overparametrized Networks
Neural networks are vulnerable to adversarial examples, i.e. inputs that...
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Adversarially Robust Generalization Just Requires More Unlabeled Data
Neural network robustness has recently been highlighted by the existence...
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A GramGaussNewton Method Learning Overparameterized Deep Neural Networks for Regression Problems
Firstorder methods such as stochastic gradient descent (SGD) are curren...
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Tianle Cai
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