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New DeepMind Framework Provides Fine-Grained Analysis of Distribution Shifts for ML Model Deployment

When researchers create a new machine learning (ML) model, they seek to avoid the overfitting problem, where the model only performs well on data distributions similar to those in its training set. Instead, the modern research focus is increasingly on building models that are robust to distribution shifts and can…

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