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Tighter Information-Theoretic Generalization Bounds from Supersamples

ICML 2023 · Learning theory and algorithms › Kernels and regression theory · approach: Information-theoretic generalization bounds · 3 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace the loss pair with its difference in the information termSupersample information-theoretic generalization bounds · Supersample settinggeneralization-bound tightnessHelped
Use the disintegrated loss-difference CMI boundSupersample generalization bounds · Binary classification with separable synthetic Gaussian databound tightnessHelped
Use the unconditional loss-difference MI boundSupersample generalization bounds · Binary classification with non-separable synthetic Gaussian databound tightnessHelped
Use the variance-based MI boundSupersample generalization bounds · Binary classification with non-separable synthetic Gaussian databound tightnessHelped
Use the sharpness-based MI boundSupersample generalization bounds · Ten-class non-separable synthetic classificationbound tightnessHelped
Use the weighted fast-rate boundSupersample generalization bounds · Separable synthetic classificationbound tightnessHelped
Use the weighted fast-rate boundSupersample generalization bounds · Non-separable binary synthetic classificationbound tightnessHurt
Use the sharpness or variance boundSupersample generalization bounds · Binary MNIST (4 vs 9)bound tightnessHelped
Use the sharpness or variance boundSupersample generalization bounds · CIFAR10 with fine-tuned ResNet-50bound tightnessHelped
Use the weighted fast-rate boundSupersample generalization bounds · Binary MNIST (4 vs 9)bound tightnessHurt
Use the sharpness bound during early SGLD trainingSupersample generalization bounds · SGLD on binary MNISTbound tightnessMixed
Use the square-root bounds rather than the binary KL boundSupersample generalization bounds · Non-separable binary synthetic classificationbound tightnessHelped

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