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Found 7 papers, 0 papers with code
Label Shift Quantification with Robustness Guarantees via Distribution Feature Matching
Quantification learning deals with the task of estimating the target label distribution under label shift.
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.
On PAC-Bayesian reconstruction guarantees for VAEs
Despite its wide use and empirical successes, the theoretical understanding and study of the behaviour and performance of the variational autoencoder (VAE) have only emerged in the past few years.
Finite sample properties of parametric MMD estimation: robustness to misspecification and dependence
Many works in statistics aim at designing a universal estimation procedure, that is, an estimator that would converge to the best approximation of the (unknown) data generating distribution in a model, without any assumption on this distribution.
MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy
In some misspecified settings, the posterior distribution in Bayesian statistics may lead to inconsistent estimates.
Convergence Rates of Variational Inference in Sparse Deep Learning
Variational inference is becoming more and more popular for approximating intractable posterior distributions in Bayesian statistics and machine learning.
A Generalization Bound for Online Variational Inference
Our work in this paper presents theoretical justifications in favor of online algorithms relying on approximate Bayesian methods.
