
Maximum likelihood estimation of regularisation parameters in highdimensional inverse problems: an empirical Bayesian approach. Part II: Theoretical Analysis
This paper presents a detailed theoretical analysis of the three stochas...
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Quantitative Propagation of Chaos for SGD in Wide Neural Networks
In this paper, we investigate the limiting behavior of a continuoustime...
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Convergence Analysis of Riemannian Stochastic Approximation Schemes
This paper analyzes the convergence for a large class of Riemannian stoc...
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Continuous and DiscreteTime Analysis of Stochastic Gradient Descent for Convex and NonConvex Functions
This paper proposes a thorough theoretical analysis of Stochastic Gradie...
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Statistical and Topological Properties of Sliced Probability Divergences
The idea of slicing divergences has been proven to be successful when co...
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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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Maximum entropy methods for texture synthesis: theory and practice
Recent years have seen the rise of convolutional neural network techniqu...
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Maximum likelihood estimation of regularisation parameters in highdimensional inverse problems: an empirical Bayesian approach
Many imaging problems require solving an inverse problem that is illcon...
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Approximate Bayesian Computation with the SlicedWasserstein Distance
Approximate Bayesian Computation (ABC) is a popular method for approxima...
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Markov Decision Process for MOOC users behavioral inference
Studies on massive open online courses (MOOCs) users discuss the existen...
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Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation
Stochastic approximation methods play a central role in maximum likeliho...
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Asymptotic Guarantees for Learning Generative Models with the SlicedWasserstein Distance
Minimum expected distance estimation (MEDE) algorithms have been widely ...
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Copulalike Variational Inference
This paper considers a new family of variational distributions motivated...
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The promises and pitfalls of Stochastic Gradient Langevin Dynamics
Stochastic Gradient Langevin Dynamics (SGLD) has emerged as a key MCMC a...
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Hypocoercivity of Piecewise Deterministic Markov ProcessMonte Carlo
In this work, we establish L^2exponential convergence for a broad class...
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Hypercoercivity of Piecewise Deterministic Markov ProcessMonte Carlo
In this paper we derive spectral gap estimates for several Piecewise Det...
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Diffusion approximations and control variates for MCMC
A new methodology is presented for the construction of control variates ...
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Piecewise Deterministic Markov Processes and their invariant measure
Piecewise Deterministic Markov Processes (PDMPs) are studied in a genera...
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SlicedWasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions
By building up on the recent theory that established the connection betw...
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Analysis of Langevin Monte Carlo via convex optimization
In this paper, we provide new insights on the Unadjusted Langevin Algori...
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Bridging the Gap between Constant Step Size Stochastic Gradient Descent and Markov Chains
We consider the minimization of an objective function given access to un...
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Highdimensional Bayesian inference via the Unadjusted Langevin Algorithm
We consider in this paper the problem of sampling a highdimensional pro...
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Alain Durmus
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