Generalization Bounds
130 papers with code • 0 benchmarks • 0 datasets
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Generalization Bounds for Message Passing Networks on Mixture of Graphons
In this more realistic and challenging scenario, we provide a generalization bound that decreases as the average number of nodes in the graphs increases.
Information-Theoretic Generalization Bounds for Deep Neural Networks
This enables refining our generalization bounds to capture the contraction as a function of the network architecture parameters.
An Information-Theoretic Framework for Out-of-Distribution Generalization
We study the Out-of-Distribution (OOD) generalization in machine learning and propose a general framework that provides information-theoretic generalization bounds.
A note on generalization bounds for losses with finite moments
Moreover, the paper derives a high-probability PAC-Bayes bound for losses with a bounded variance.
Generalization of Scaled Deep ResNets in the Mean-Field Regime
To derive the generalization bounds under this setting, our analysis necessitates a shift from the conventional time-invariant Gram matrix employed in the lazy training regime to a time-variant, distribution-dependent version.
Towards Robust Out-of-Distribution Generalization Bounds via Sharpness
To bridge this gap between optimization and OOD generalization, we study the effect of sharpness on how a model tolerates data change in domain shift which is usually captured by "robustness" in generalization.
Generalization of Graph Neural Networks through the Lens of Homomorphism
In this work, we propose to study the generalization of GNNs through a novel perspective - analyzing the entropy of graph homomorphism.
On Generalization Bounds for Deep Compound Gaussian Neural Networks
In this paper, we develop novel generalization error bounds for a class of unrolled DNNs that are informed by a compound Gaussian prior.
Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization
In this work, we investigate the interplay between memorization and learning in the context of \emph{stochastic convex optimization} (SCO).
Active Few-Shot Fine-Tuning
We study the active few-shot fine-tuning of large neural networks to downstream tasks.
