Related papers: Entropic Mirror Monte Carlo

Entropic Mirror Monte Carlo

URL: http://arxiv.org/abs/2602.03165v1
Date: Tue, 03 Feb 2026 06:32:35 GMT
Title: Entropic Mirror Monte Carlo
Authors: Anas Cherradi, Yazid Janati, Alain Durmus, Sylvain Le Corff, Yohan Petetin, Julien Stoehr,
Abstract summary: In this paper, we propose a novel adaptive scheme for the construction of efficient proposal distributions.<n>Our algorithm promotes efficient exploration of the target distribution by combining global sampling mechanisms with a delayed weighting procedure.
Score: 15.556187883029937
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Importance sampling is a Monte Carlo method which designs estimators of expectations under a target distribution using weighted samples from a proposal distribution. When the target distribution is complex, such as multimodal distributions in highdimensional spaces, the efficiency of importance sampling critically depends on the choice of the proposal distribution. In this paper, we propose a novel adaptive scheme for the construction of efficient proposal distributions. Our algorithm promotes efficient exploration of the target distribution by combining global sampling mechanisms with a delayed weighting procedure. The proposed weighting mechanism plays a key role by enabling rapid resampling in regions where the proposal distribution is poorly adapted to the target. Our sampling algorithm is shown to be geometrically convergent under mild assumptions and is illustrated through various numerical experiments.

Related papers

Enhanced Importance Sampling through Latent Space Exploration in Normalizing Flows [69.8873421870522]
importance sampling is a rare event simulation technique used in Monte Carlo simulations.<n>We propose a method for more efficient sampling by updating the proposal distribution in the latent space of a normalizing flow.
arXiv Detail & Related papers (2025-01-06T21:18:02Z)
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers [143.6249073384419]
In this paper, we propose a distributed sampling scheme based on the alternating direction method of multipliers. We provide both theoretical guarantees of our algorithm's convergence and experimental evidence of its superiority to the state-of-the-art. In simulation, we deploy our algorithm on linear and logistic regression tasks and illustrate its fast convergence compared to existing gradient-based methods.
arXiv Detail & Related papers (2024-01-29T02:08:40Z)
Adaptive Annealed Importance Sampling with Constant Rate Progress [68.8204255655161]
Annealed Importance Sampling (AIS) synthesizes weighted samples from an intractable distribution. We propose the Constant Rate AIS algorithm and its efficient implementation for $alpha$-divergences.
arXiv Detail & Related papers (2023-06-27T08:15:28Z)
Score-Based Diffusion meets Annealed Importance Sampling [89.92133671626327]
Annealed Importance Sampling remains one of the most effective methods for marginal likelihood estimation. We leverage recent progress in score-based generative modeling to approximate the optimal extended target distribution for AIS proposals.
arXiv Detail & Related papers (2022-08-16T12:13:29Z)
Sampling from Discrete Energy-Based Models with Quality/Efficiency Trade-offs [3.491202838583993]
Energy-Based Models (EBMs) allow for extremely flexible specifications of probability distributions. They do not provide a mechanism for obtaining exact samples from these distributions. We propose a new approximate sampling technique, Quasi Rejection Sampling (QRS), that allows for a trade-off between sampling efficiency and sampling quality.
arXiv Detail & Related papers (2021-12-10T17:51:37Z)
LSB: Local Self-Balancing MCMC in Discrete Spaces [2.385916960125935]
This work considers using machine learning to adapt the proposal distribution to the target, in order to improve the sampling efficiency in the purely discrete domain. We call the resulting sampler as the Locally Self-Balancing Sampler (LSB)
arXiv Detail & Related papers (2021-09-08T18:31:26Z)
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence [77.06203118175335]
Variational Inference (VI) is a popular alternative to exact sampling in Bayesian inference. Importance sampling (IS) is often used to fine-tune and de-bias the estimates of approximate Bayesian inference procedures. We propose a novel combination of optimization and sampling techniques for approximate Bayesian inference.
arXiv Detail & Related papers (2021-06-30T11:00:24Z)
Achieving Efficiency in Black Box Simulation of Distribution Tails with Self-structuring Importance Samplers [1.6114012813668934]
The paper presents a novel Importance Sampling (IS) scheme for estimating distribution of performance measures modeled with a rich set of tools such as linear programs, integer linear programs, piecewise linear/quadratic objectives, feature maps specified with deep neural networks, etc.
arXiv Detail & Related papers (2021-02-14T03:37:22Z)
A Neural Network MCMC sampler that maximizes Proposal Entropy [3.4698840925433765]
Augmenting samplers with neural networks can potentially improve their efficiency. Our network architecture utilizes the gradient of the target distribution for generating proposals. The adaptive sampler achieves unbiased sampling with significantly higher proposal entropy than Langevin dynamics sampler.
arXiv Detail & Related papers (2020-10-07T18:01:38Z)
Distributionally Robust Bayesian Quadrature Optimization [60.383252534861136]
We study BQO under distributional uncertainty in which the underlying probability distribution is unknown except for a limited set of its i.i.d. samples. A standard BQO approach maximizes the Monte Carlo estimate of the true expected objective given the fixed sample set. We propose a novel posterior sampling based algorithm, namely distributionally robust BQO (DRBQO) for this purpose.
arXiv Detail & Related papers (2020-01-19T12:00:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.