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Found 10 papers, 2 papers with code
Confidence Sequences for Generalized Linear Models via Regret Analysis
We develop a methodology for constructing confidence sets for parameters of statistical models via a reduction to sequential prediction.
Online-to-PAC generalization bounds under graph-mixing dependencies
Traditional generalization results in statistical learning require a training data set made of independently drawn examples.
Generalization bounds for mixing processes via delayed online-to-PAC conversions
We study the generalization error of statistical learning algorithms in a non-i. i. d.
A note on regularised NTK dynamics with an application to PAC-Bayesian training
We establish explicit dynamics for neural networks whose training objective has a regularising term that constrains the parameters to remain close to their initial value.
Generalisation under gradient descent via deterministic PAC-Bayes
We establish disintegrated PAC-Bayesian generalisation bounds for models trained with gradient descent methods or continuous gradient flows.
Chained Generalisation Bounds
This work discusses how to derive upper bounds for the expected generalisation error of supervised learning algorithms by means of the chaining technique.
Conditionally Gaussian PAC-Bayes
Recent studies have empirically investigated different methods to train stochastic neural networks on a classification task by optimising a PAC-Bayesian bound via stochastic gradient descent.
Wide stochastic networks: Gaussian limit and PAC-Bayesian training
The limit of infinite width allows for substantial simplifications in the analytical study of over-parameterised neural networks.
