Diffusion at Absolute Zero: Langevin Sampling Using Successive Moreau Envelopes
- URL: http://arxiv.org/abs/2502.01358v1
- Date: Mon, 03 Feb 2025 13:50:57 GMT
- Title: Diffusion at Absolute Zero: Langevin Sampling Using Successive Moreau Envelopes
- Authors: Andreas Habring, Alexander Falk, Thomas Pock,
- Abstract summary: We propose a novel method for sampling from Gibbs distributions of the form $pi(x)proptoexp(-U(x))$ with a potential $U(x)$.
Inspired by diffusion models we propose to consider a sequence $(pit_k)_k$ of approximations of the target density, for which $pit_kapprox pi$ for $k$ small and, on the other hand, $pit_k$ exhibits favorable properties for sampling for $k$ large.
- Score: 52.69179872700035
- License:
- Abstract: In this article we propose a novel method for sampling from Gibbs distributions of the form $\pi(x)\propto\exp(-U(x))$ with a potential $U(x)$. In particular, inspired by diffusion models we propose to consider a sequence $(\pi^{t_k})_k$ of approximations of the target density, for which $\pi^{t_k}\approx \pi$ for $k$ small and, on the other hand, $\pi^{t_k}$ exhibits favorable properties for sampling for $k$ large. This sequence is obtained by replacing parts of the potential $U$ by its Moreau envelopes. Sampling is performed in an Annealed Langevin type procedure, that is, sequentially sampling from $\pi^{t_k}$ for decreasing $k$, effectively guiding the samples from a simple starting density to the more complex target. In addition to a theoretical analysis we show experimental results supporting the efficacy of the method in terms of increased convergence speed and applicability to multi-modal densities $\pi$.
Related papers
- Polynomial time sampling from log-smooth distributions in fixed dimension under semi-log-concavity of the forward diffusion with application to strongly dissipative distributions [9.48556659249574]
We provide a sampling algorithm with complexity in fixed dimension.
We prove that our algorithm achieves an expected $epsilon$ error in $KL$ divergence.
As an application, we derive an exponential complexity improvement for the problem of sampling from an $L$-log-smooth distribution.
arXiv Detail & Related papers (2024-12-31T17:51:39Z) - Parallel simulation for sampling under isoperimetry and score-based diffusion models [56.39904484784127]
As data size grows, reducing the iteration cost becomes an important goal.
Inspired by the success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks.
Our work highlights the potential advantages of simulation methods in scientific computation for dynamics-based sampling and diffusion models.
arXiv Detail & Related papers (2024-12-10T11:50:46Z) - Log-Concave Coupling for Sampling Neural Net Posteriors [0.4604003661048266]
We present a sampling algorithm for single hidden layer neural networks.
The algorithm is based on a coupling of the posterior density for $w$ with an auxiliary random variable $xi$.
The score of the marginal density of the auxiliary random variable $xi$ is determined by an expectation over $w|xi$.
arXiv Detail & Related papers (2024-07-26T15:05:41Z) - Provable Benefit of Annealed Langevin Monte Carlo for Non-log-concave Sampling [28.931489333515618]
We establish an oracle complexity of $widetildeOleft(fracdbeta2cal A2varepsilon6right)$ for the simple annealed Langevin Monte Carlo algorithm.
We show that $cal A$ represents the action of a curve of probability measures interpolating the target distribution $pi$ and a readily sampleable distribution.
arXiv Detail & Related papers (2024-07-24T02:15:48Z) - Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel [10.840582511203024]
We show that our algorithm can be parallelized to run in only $widetilde O(log2 d)$ parallel rounds.
We also show that our algorithm can be parallelized to run in only $widetilde O(log2 d)$ parallel rounds.
arXiv Detail & Related papers (2024-06-03T01:34:34Z) - Sharper Rates and Flexible Framework for Nonconvex SGD with Client and
Data Sampling [64.31011847952006]
We revisit the problem of finding an approximately stationary point of the average of $n$ smooth and possibly non-color functions.
We generalize the $smallsfcolorgreen$ so that it can provably work with virtually any sampling mechanism.
We provide the most general and most accurate analysis of optimal bound in the smooth non-color regime.
arXiv Detail & Related papers (2022-06-05T21:32:33Z) - Approximate Function Evaluation via Multi-Armed Bandits [51.146684847667125]
We study the problem of estimating the value of a known smooth function $f$ at an unknown point $boldsymbolmu in mathbbRn$, where each component $mu_i$ can be sampled via a noisy oracle.
We design an instance-adaptive algorithm that learns to sample according to the importance of each coordinate, and with probability at least $1-delta$ returns an $epsilon$ accurate estimate of $f(boldsymbolmu)$.
arXiv Detail & Related papers (2022-03-18T18:50:52Z) - Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity
Guarantees for Langevin Monte Carlo [24.00911089902082]
This is the sampling iterations for finding an $pi exp(-V)$ on $mathd.
We discuss numerous extensions applications; in particular, it is the first step towards the general theory of non-concave sampling.
arXiv Detail & Related papers (2022-02-10T18:20:55Z) - The Sample Complexity of Robust Covariance Testing [56.98280399449707]
We are given i.i.d. samples from a distribution of the form $Z = (1-epsilon) X + epsilon B$, where $X$ is a zero-mean and unknown covariance Gaussian $mathcalN(0, Sigma)$.
In the absence of contamination, prior work gave a simple tester for this hypothesis testing task that uses $O(d)$ samples.
We prove a sample complexity lower bound of $Omega(d2)$ for $epsilon$ an arbitrarily small constant and $gamma
arXiv Detail & Related papers (2020-12-31T18:24:41Z) - Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and
Variance Reduction [63.41789556777387]
Asynchronous Q-learning aims to learn the optimal action-value function (or Q-function) of a Markov decision process (MDP)
We show that the number of samples needed to yield an entrywise $varepsilon$-accurate estimate of the Q-function is at most on the order of $frac1mu_min (1-gamma)5varepsilon2+ fract_mixmu_min (1-gamma)$ up to some logarithmic factor.
arXiv Detail & Related papers (2020-06-04T17:51:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.