Unadjusted Langevin algorithm for sampling a mixture of weakly smooth potentials
From MaRDI portal
Publication:2083423
DOI10.1214/22-BJPS538MaRDI QIDQ2083423
Publication date: 10 October 2022
Published in: Brazilian Journal of Probability and Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2112.09311
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Cites Work
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- Euler approximations with varying coefficients: the case of superlinearly growing diffusion coefficients
- Reflection couplings and contraction rates for diffusions
- Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients
- A note on tamed Euler approximations
- Weighted Csiszár-Kullback-Pinsker inequalities and applications to transportation inequalities
- Dispersion of mass and the complexity of randomized geometric algorithms
- Comparison theorems for differential equations
- Isoperimetric and analytic inequalities for log-concave probability measures
- Normalizing constants of log-concave densities
- Logarithmic Sobolev inequalities and stochastic Ising models
- Unadjusted Langevin algorithm for sampling a mixture of weakly smooth potentials
- On stochastic gradient Langevin dynamics with dependent data streams in the logconcave case
- The tamed unadjusted Langevin algorithm
- User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
- The Poincaré inequality and quadratic transportation-variance inequalities
- High-dimensional Bayesian inference via the unadjusted Langevin algorithm
- Higher order Langevin Monte Carlo algorithm
- Random gradient-free minimization of convex functions
- Nonasymptotic convergence analysis for the unadjusted Langevin algorithm
- Nonasymptotic bounds for sampling algorithms without log-concavity
- Wasserstein Continuity of Entropy and Outer Bounds for Interference Channels
- The geometry of logconcave functions and sampling algorithms
- A random polynomial-time algorithm for approximating the volume of convex bodies
- Quantitative Harris-type theorems for diffusions and McKean–Vlasov processes
- Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau
- On Stochastic Gradient Langevin Dynamics with Dependent Data Streams: The Fully Nonconvex Case
- Stochastic Processes and Applications
- Sampling can be faster than optimization
- Prediction, Learning, and Games
- Theoretical Guarantees for Approximate Sampling from Smooth and Log-Concave Densities
- Optimal Transport
