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08/15/2023
Monte Carlo guided Diffusion for Bayesian linear inverse problems
Ill-posed linear inverse problems that combine knowledge of the forward ...
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07/10/2023
Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference
We provide a rigorous analysis of training by variational inference (VI)...
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06/08/2023
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
Federated Learning (FL) is a machine learning framework where many clien...
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06/01/2023
Balanced Training of Energy-Based Models with Adaptive Flow Sampling
Energy-based models (EBMs) are versatile density estimation models that ...
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04/27/2023
One-Step Distributional Reinforcement Learning
Reinforcement learning (RL) allows an agent interacting sequentially wit...
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04/01/2023
Restarted Bayesian Online Change-point Detection for Non-Stationary Markov Decision Processes
We consider the problem of learning in a non-stationary reinforcement le...
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03/16/2023
Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold
We consider the problem of minimizing a non-convex function over a smoot...
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03/14/2023
Fast Rates for Maximum Entropy Exploration
We consider the reinforcement learning (RL) setting, in which the agent ...
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03/10/2023
Rosenthal-type inequalities for linear statistics of Markov chains
In this paper, we establish novel deviation bounds for additive function...
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02/22/2023
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning
Stochastic approximation (SA) is a classical algorithm that has had sinc...
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02/09/2023
On Sampling with Approximate Transport Maps
Transport maps can ease the sampling of distributions with non-trivial g...
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01/02/2023
State and parameter learning with PaRIS particle Gibbs
Non-linear state-space models, also known as general hidden Markov model...
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01/02/2023
Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization
This paper introduces a novel algorithm, the Perturbed Proximal Precondi...
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11/07/2022
AskewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks
In this paper, we develop a new algorithm, Annealed Skewed SGD - AskewSG...
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10/31/2022
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms
This paper focuses on Bayesian inference in a federated learning context...
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09/28/2022
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
We consider reinforcement learning in an environment modeled by an episo...
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09/21/2022
Particle-based, rapid incremental smoother meets particle Gibbs
The particle-based, rapid incremental smoother (PARIS) is a sequential M...
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07/13/2022
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling
Importance Sampling (IS) is a method for approximating expectations unde...
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07/10/2022
Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation
This paper provides a finite-time analysis of linear stochastic approxim...
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07/08/2022
Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study
This paper studies the Variational Inference (VI) used for training Baye...
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06/07/2022
FedPop: A Bayesian Approach for Personalised Federated Learning
Personalised federated learning (FL) aims at collaboratively learning a ...
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05/16/2022
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
We propose the Bayes-UCBVI algorithm for reinforcement learning in tabul...
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01/06/2022
On the geometric convergence for MALA under verifiable conditions
While the Metropolis Adjusted Langevin Algorithm (MALA) is a popular and...
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11/04/2021
Ex^2MCMC: Sampling through Exploration Exploitation
We develop an Explore-Exploit Markov chain Monte Carlo algorithm (Ex^2MC...
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11/03/2021
Federated Expectation Maximization with heterogeneity mitigation and variance reduction
The Expectation Maximization (EM) algorithm is the default algorithm for...
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07/30/2021
Uniform minorization condition and convergence bounds for discretizations of kinetic Langevin dynamics
We study the convergence in total variation and V-norm of discretization...
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06/30/2021
Monte Carlo Variational Auto-Encoders
Variational auto-encoders (VAE) are popular deep latent variable models ...
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06/11/2021
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs
Performing reliable Bayesian inference on a big data scale is becoming a...
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06/02/2021
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
This paper provides a non-asymptotic analysis of linear stochastic appro...
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06/01/2021
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning
Federated learning aims at conducting inference when data are decentrali...
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05/25/2021
The Perturbed Prox-Preconditioned SPIDER algorithm for EM-based large scale learning
Incremental Expectation Maximization (EM) algorithms were introduced to ...
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03/17/2021
Invertible Flow Non Equilibrium sampling
Simultaneously sampling from a complex distribution with intractable nor...
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02/15/2021
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size
This paper studies fixed step-size stochastic approximation (SA) schemes...
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01/30/2021
Rates of convergence for density estimation with GANs
We undertake a precise study of the non-asymptotic properties of vanilla...
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01/30/2021
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning
This paper studies the exponential stability of random matrix products d...
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12/31/2020
Nonreversible MCMC from conditional invertible transforms: a complete recipe with convergence guarantees
Markov Chain Monte Carlo (MCMC) is a class of algorithms to sample compl...
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11/30/2020
A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm
The Expectation Maximization (EM) algorithm is of key importance for inf...
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11/24/2020
Geom-SPIDER-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization
The Expectation Maximization (EM) algorithm is a key reference for infer...
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05/27/2020
Convergence Analysis of Riemannian Stochastic Approximation Schemes
This paper analyzes the convergence for a large class of Riemannian stoc...
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02/27/2020
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference
In this contribution, we propose a new computationally efficient method ...
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02/04/2020
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise
Linear two-timescale stochastic approximation (SA) scheme is an importan...
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01/22/2020
On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation
Uncertainty quantification for deep learning is a challenging open probl...
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10/30/2019
Unifying mirror descent and dual averaging
We introduce and analyse a new family of algorithms which generalizes an...
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10/28/2019
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods
The EM algorithm is one of the most popular algorithm for inference in l...
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10/27/2019
f-SAEM: A fast Stochastic Approximation of the EM algorithm for nonlinear mixed effects models
The ability to generate samples of the random effects from their conditi...
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07/05/2019
A quantitative Mc Diarmid's inequality for geometrically ergodic Markov chains
We state and prove a quantitative version of the bounded difference ineq...
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05/30/2019
On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case
We consider the problem of sampling from a target distribution which is ...
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02/02/2019
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme
Stochastic approximation (SA) is a key method used in statistical learni...
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12/20/2018
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
Many applications of machine learning involve the analysis of large data...
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11/25/2018