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Generalization bounds: Perspectives from information theory and PAC-Bayes
A fundamental question in theoretical machine learning is generalization. Over the past
decades, the PAC-Bayesian approach has been established as a flexible framework to …
decades, the PAC-Bayesian approach has been established as a flexible framework to …
Information complexity of stochastic convex optimization: Applications to generalization and memorization
I Attias, GK Dziugaite, M Haghifam, R Livni… - ar** a theoretical understanding of meta-learning. Given multiple tasks
drawn iid from some (unknown) task distribution, the goal is to find a good pre-trained model …
drawn iid from some (unknown) task distribution, the goal is to find a good pre-trained model …
Bayes meets Bernstein at the meta level: an analysis of fast rates in meta-learning with PAC-Bayes
Bernstein's condition is a key assumption that guarantees fast rates in machine learning. For
example, the Gibbs algorithm with prior $\pi $ has an excess risk in $ O (d_ {\pi}/n) $, as …
example, the Gibbs algorithm with prior $\pi $ has an excess risk in $ O (d_ {\pi}/n) $, as …
Exactly tight information-theoretic generalization error bound for the quadratic gaussian problem
We provide a new information-theoretic generalization error bound that is exactly tight (ie,
matching even the constant) for the canonical quadratic Gaussian (location) problem. Most …
matching even the constant) for the canonical quadratic Gaussian (location) problem. Most …
Learning an explicit hyper-parameter prediction function conditioned on tasks
Meta learning has attracted much attention recently in machine learning community.
Contrary to conventional machine learning aiming to learn inherent prediction rules to …
Contrary to conventional machine learning aiming to learn inherent prediction rules to …
More flexible pac-bayesian meta-learning by learning learning algorithms
We introduce a new framework for studying meta-learning methods using PAC-Bayesian
theory. Its main advantage over previous work is that it allows for more flexibility in how the …
theory. Its main advantage over previous work is that it allows for more flexibility in how the …