Bernstein's condition is a key assumption that guarantees fast rates in
...
This paper introduces a new principled approach for offline policy
optim...
We study the deviation inequality for a sum of high-dimensional random
m...
An important feature of kernel mean embeddings (KME) is that the rate of...
The aim of reduced rank regression is to connect multiple response varia...
Classical implementations of approximate Bayesian computation (ABC) empl...
Aggregated predictors are obtained by making a set of basic predictors v...
Although genome-wide association studies (GWAS) on complex traits have
a...
We introduce a class of Markov chains, that contains the model of stocha...
Initially designed for independent datas, low-rank matrix completion was...
Online gradient methods, like the online gradient algorithm (OGA), often...
Continual learning (CL) is a setting in which an agent has to learn from...
This paper deals with robust inference for parametric copula models.
Est...
We tackle the problem of online optimization with a general, possibly
un...
Many datasets are collected automatically, and are thus easily contamina...
Many works in statistics aim at designing a universal estimation procedu...
In some misspecified settings, the posterior distribution in Bayesian
st...
We propose a vector auto-regressive (VAR) model with a low-rank constrai...
Bayesian inference provides an attractive online-learning framework to
a...
Matrix factorization is a powerful data analysis tool. It has been used ...
Exponential inequalities are main tools in machine learning theory. To p...
Mixture models are widely used in Bayesian statistics and machine learni...
We consider the problem of transfer learning in an online setting. Diffe...
PAC-Bayesian learning bounds are of the utmost interest to the learning
...
Due to challenging applications such as collaborative filtering, the mat...
The aim of this paper is to provide some theoretical understanding of
Ba...
The PAC-Bayesian approach is a powerful set of techniques to derive non-...
We develop a scoring and classification procedure based on the PAC-Bayes...
The problem of low-rank matrix estimation recently received a lot of
att...
The aim of this paper is to generalize the PAC-Bayesian theorems proved ...