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Redundant features can hurt robustness to distribution shift

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Multiple testing with test statistics following heavy-tailed distributions

Zhiwen Jiang

In multiple testing problems where the components come from a mixture model of noise and true effect, we seek to first test for the existence of the non-zero components, and then identify the true alternatives under a fixed significance level

\alpha

. Two ...

EPFL2021

Redundant features can hurt robustness to distribution shift

Graph Chatbot

Chattez avec Graph Search

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Feature distribution learning by passive exposure

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Robust Generalization despite Distribution Shift via Minimum Discriminating Information

Multiple testing with test statistics following heavy-tailed distributions

Learning Robust and Adaptive Representations: from Interactions, for Interactions

Robustness and invariance properties of image classifiers

Towards Robust Vision Transformer

Multiple testing with test statistics following heavy-tailed distributions

OptTTA: Learnable Test-Time Augmentation for Source-Free Medical Image Segmentation Under Domain Shift

Making Computer Vision Models Robust and Adaptive

Federated Learning under Covariate Shifts with Generalization Guarantees

Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback

Feature distribution learning by passive exposure

Robust Generalization despite Distribution Shift via Minimum Discriminating Information