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02/23/2023
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 ...
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10/24/2022
PAC-Bayesian Offline Contextual Bandits With Guarantees
This paper introduces a new principled approach for offline policy optim...
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10/18/2022
Dimension-free Bounds for Sum of Dependent Matrices and Operators with Heavy-Tailed Distribution
We study the deviation inequality for a sum of high-dimensional random m...
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10/13/2022
Variance-Aware Estimation of Kernel Mean Embedding
An important feature of kernel mean embeddings (KME) is that the rate of...
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06/17/2022
Optimal quasi-Bayesian reduced rank regression with incomplete response
The aim of reduced rank regression is to connect multiple response varia...
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06/14/2022
Concentration and robustness of discrepancy-based ABC via Rademacher complexity
Classical implementations of approximate Bayesian computation (ABC) empl...
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10/21/2021
User-friendly introduction to PAC-Bayes bounds
Aggregated predictors are obtained by making a set of basic predictors v...
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08/12/2021
Understanding the population structure correction regression
Although genome-wide association studies (GWAS) on complex traits have a...
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02/17/2021
Deviation inequalities for stochastic approximation by averaging
We introduce a class of Markov chains, that contains the model of stocha...
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02/15/2021
Tight Risk Bound for High Dimensional Time Series Completion
Initially designed for independent datas, low-rank matrix completion was...
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02/04/2021
Meta-strategy for Learning Tuning Parameters with Guarantees
Online gradient methods, like the online gradient algorithm (OGA), often...
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10/07/2020
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix
Continual learning (CL) is a setting in which an agent has to learn from...
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10/01/2020
Estimation of copulas via Maximum Mean Discrepancy
This paper deals with robust inference for parametric copula models. Est...
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09/07/2020
Non-exponentially weighted aggregation: regret bounds for unbounded loss functions
We tackle the problem of online optimization with a general, possibly un...
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06/01/2020
Universal Robust Regression via Maximum Mean Discrepancy
Many datasets are collected automatically, and are thus easily contamina...
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12/12/2019
Finite sample properties of parametric MMD estimation: robustness to misspecification and dependence
Many works in statistics aim at designing a universal estimation procedu...
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09/29/2019
MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy
In some misspecified settings, the posterior distribution in Bayesian st...
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05/02/2019
High dimensional VAR with low rank transition
We propose a vector auto-regressive (VAR) model with a low-rank constrai...
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04/08/2019
A Generalization Bound for Online Variational Inference
Bayesian inference provides an attractive online-learning framework to a...
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03/13/2019
Matrix factorization for multivariate time series analysis
Matrix factorization is a powerful data analysis tool. It has been used ...
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08/27/2018
Exponential inequalities for nonstationary Markov Chains
Exponential inequalities are main tools in machine learning theory. To p...
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05/14/2018
Consistency of Variational Bayes Inference for Estimation and Model Selection in Mixtures
Mixture models are widely used in Bayesian statistics and machine learni...
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10/27/2016
Regret Bounds for Lifelong Learning
We consider the problem of transfer learning in an online setting. Diffe...
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10/23/2016
Simpler PAC-Bayesian Bounds for Hostile Data
PAC-Bayesian learning bounds are of the utmost interest to the learning ...
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04/14/2016
1-bit Matrix Completion: PAC-Bayesian Analysis of a Variational Approximation
Due to challenging applications such as collaborative filtering, the mat...
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01/06/2016
An Oracle Inequality for Quasi-Bayesian Non-Negative Matrix Factorization
The aim of this paper is to provide some theoretical understanding of Ba...
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06/12/2015
On the properties of variational approximations of Gibbs posteriors
The PAC-Bayesian approach is a powerful set of techniques to derive non-...
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10/07/2014
PAC-Bayesian AUC classification and scoring
We develop a scoring and classification procedure based on the PAC-Bayes...
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06/17/2013
Bayesian methods for low-rank matrix estimation: short survey and theoretical study
The problem of low-rank matrix estimation recently received a lot of att...
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12/11/2007
