7 citations · 19 across the 21 of their papers we have counts for
28 papers
PAC-Bayesian Reconstruction Guarantees for Time Series Variational Autoencoders
Chloé Hashimoto-Cullen, Ghislain Agoua, Benjamin Guedj +1
Forecasting time series accurately is critical for applications with complex data ranging from energy systems to healthcare and finance. Among current state of the art models, gene…
Theoretical Guarantees for SMC-Guided Diffusion Sampling
Stanislas Strasman, Gabriel Victorino Cardoso, Sylvain Le Corff +2
Post-hoc conditioning of pretrained diffusion models can be addressed using Sequential Monte Carlo (SMC) methods. By evolving an interacting particle system, SMC-guided diffusion s…
Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models
Stanislas Strasman, Sobihan Surendran, Sylvain Le Corff
Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications. While the statistical properties of their sampling…
On Forgetting and Stability of Score-based Generative models
Stanislas Strasman, Gabriel Cardoso, Sylvain Le Corff +2
Understanding the stability and long-time behavior of generative models is a fundamental problem in modern machine learning. This paper provides quantitative bounds on the sampling…
Independent Component Discovery in Temporal Count Data
Alexandre Chaussard, Anna Bonnet, Sylvain Le Corff
Advances in data collection are producing growing volumes of temporal count observations, making adapted modeling increasingly necessary. In this work, we introduce a generative fr…
TaxaPLN: a taxonomy-aware augmentation strategy for microbiome-trait classification including metadata
Alexandre Chaussard, Anna Bonnet, Sylvain Le Corff +1
The gut microbiome plays a crucial role in human health, making it a corner stone of modern biomedical research. To study its structure and dynamics, machine learning models are in…
Latent Guided Sampling for Combinatorial Optimization
Sobihan Surendran, Adeline Fermanian, Sylvain Le Corff
Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature makes them computationally challenging.…
Tree-based variational inference for Poisson log-normal models
Alexandre Chaussard, Anna Bonnet, Elisabeth Gassiat +1
When studying ecosystems, hierarchical trees are often used to organize entities based on proximity criteria, such as the taxonomy in microbiology, social classes in geography, or…
Variational quantization for state space models
Etienne David, Jean Bellot, Sylvain Le Corff
Forecasting tasks using large datasets gathering thousands of heterogeneous time series is a crucial statistical problem in numerous sectors. The main challenge is to model a rich…
Diffusion posterior sampling for simulation-based inference in tall data settings
Julia Linhart, Gabriel Victorino Cardoso, Alexandre Gramfort +2
Identifying the parameters of a non-linear model that best explain observed data is a core task across scientific fields. When such models rely on complex simulators, evaluating th…
An analysis of the noise schedule for score-based generative models
Stanislas Strasman, Antonio Ocello, Claire Boyer +2
Score-based generative models (SGMs) aim at estimating a target data distribution by learning score functions using only noise-perturbed samples from the target.Recent literature h…
Non-asymptotic Analysis of Biased Adaptive Stochastic Approximation
Sobihan Surendran, Antoine Godichon-Baggioni, Adeline Fermanian +1
Stochastic Gradient Descent (SGD) with adaptive steps is widely used to train deep neural networks and generative models. Most theoretical results assume that it is possible to obt…
Variational excess risk bound for general state space models
Élisabeth Gassiat, Sylvain Le Corff
In this paper, we consider variational autoencoders (VAE) for general state space models. We consider a backward factorization of the variational distributions to analyze the exces…
Asymptotic convergence of iterative optimization algorithms
Randal Douc, Sylvain Le Corff
This paper introduces a general framework for iterative optimization algorithms and establishes under general assumptions that their convergence is asymptotically geometric. We als…
Amortized backward variational inference in nonlinear state-space models
Mathis Chagneux, Élisabeth Gassiat, Pierre Gloaguen +1
We consider the problem of state estimation in general state-space models using variational inference. For a generic variational family defined using the same backward decompositio…
HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series
Etienne David, Jean Bellot, Sylvain Le Corff
Developing models and algorithms to predict nonstationary time series is a long standing statistical problem. It is crucial for many applications, in particular for fashion or reta…
Backward importance sampling for online estimation of state space models
Alice Martin, Marie-Pierre Etienne, Pierre Gloaguen +2
This paper proposes a new Sequential Monte Carlo algorithm to perform online estimation in the context of state space models when either the transition density of the latent state…
Learning the distribution of latent variables in paired comparison models with round-robin scheduling
Roland Diel, Sylvain Le Corff, Matthieu Lerasle
Paired comparison data considered in this paper originate from the comparison of a large number N of individuals in couples. The dataset is a collection of results of contests betw…
Online Sequential Monte Carlo smoother for partially observed stochastic differential equations
Pierre Gloaguen, Marie-Pierre Etienne, Sylvain Le Corff
This paper introduces a new algorithm to approximate smoothed additive functionals for partially observed stochastic differential equations. This method relies on a recent procedur…
Stochastic differential equation based on a multimodal potential to model movement data in ecology
Pierre Gloaguen, Marie-Pierre Etienne, Sylvain Le Corff
This paper proposes a new model for individuals movement in ecology. The movement process is defined as a solution to a stochastic differential equation whose drift is the gradient…
Consistent estimation of the filtering and marginal smoothing distributions in nonparametric hidden Markov models
Yohann De Castro, Elisabeth Gassiat, Sylvain Le Corff
In this paper, we consider the filtering and smoothing recursions in nonparametric finite state space hidden Markov models (HMMs) when the parameters of the model are unknown and r…
Statistical Inference for Oscillation Processes
Rainer Dahlhaus, Thierry Dumont, Sylvain Le Corff +1
A new model for time series with a specific oscillation pattern is proposed. The model consists of a hidden phase process controlling the speed of polling and a nonparametric curve…
A shrinkage-thresholding Metropolis adjusted Langevin algorithm for Bayesian variable selection
Amandine Schreck, Gersende Fort, Sylvain Le Corff +1
This paper introduces a new Markov Chain Monte Carlo method for Bayesian variable selection in high dimensional settings. The algorithm is a Hastings-Metropolis sampler with a prop…
Nonparametric regression on hidden phi-mixing variables: identifiability and consistency of a pseudo-likelihood based estimation procedure
Thierry Dumont, Sylvain Le Corff
This paper outlines a new nonparametric estimation procedure for unobserved phi-mixing processes. It is assumed that the only information on the stationary hidden states (Xk) is gi…