Correction to "Wasserstein distance estimates for the distributions of
numerical approximations to ergodic stochastic differential equations"
- URL: http://arxiv.org/abs/2402.08711v2
- Date: Thu, 15 Feb 2024 08:34:41 GMT
- Title: Correction to "Wasserstein distance estimates for the distributions of
numerical approximations to ergodic stochastic differential equations"
- Authors: Daniel Paulin, Peter A. Whalley
- Abstract summary: method for analyzing non-asymptotic guarantees of numerical discretizations of ergodic SDEs in Wasserstein-2 distance is presented.
- Score: 1.2691047660244337
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A method for analyzing non-asymptotic guarantees of numerical discretizations
of ergodic SDEs in Wasserstein-2 distance is presented by Sanz-Serna and
Zygalakis in ``Wasserstein distance estimates for the distributions of
numerical approximations to ergodic stochastic differential equations". They
analyze the UBU integrator which is strong order two and only requires one
gradient evaluation per step, resulting in desirable non-asymptotic guarantees,
in particular $\mathcal{O}(d^{1/4}\epsilon^{-1/2})$ steps to reach a distance
of $\epsilon > 0$ in Wasserstein-2 distance away from the target distribution.
However, there is a mistake in the local error estimates in Sanz-Serna and
Zygalakis (2021), in particular, a stronger assumption is needed to achieve
these complexity estimates. This note reconciles the theory with the dimension
dependence observed in practice in many applications of interest.
Related papers
- Relative-Translation Invariant Wasserstein Distance [82.6068808353647]
We introduce a new family of distances, relative-translation invariant Wasserstein distances ($RW_p$)
We show that $RW_p distances are also real distance metrics defined on the quotient set $mathcalP_p(mathbbRn)/sim$ invariant to distribution translations.
arXiv Detail & Related papers (2024-09-04T03:41:44Z) - Squared Wasserstein-2 Distance for Efficient Reconstruction of
Stochastic Differential Equations [0.0]
We provide an analysis of the squared $W$ distance between two probability distributions associated with Wasserstein differential equations (SDEs)
Based on this analysis, we propose the use of a squared $W$ distance-based loss functions in the textitreconstruction of SDEs from noisy data.
arXiv Detail & Related papers (2024-01-21T00:54:50Z) - Mean-Square Analysis with An Application to Optimal Dimension Dependence
of Langevin Monte Carlo [60.785586069299356]
This work provides a general framework for the non-asymotic analysis of sampling error in 2-Wasserstein distance.
Our theoretical analysis is further validated by numerical experiments.
arXiv Detail & Related papers (2021-09-08T18:00:05Z) - Large-Scale Wasserstein Gradient Flows [84.73670288608025]
We introduce a scalable scheme to approximate Wasserstein gradient flows.
Our approach relies on input neural networks (ICNNs) to discretize the JKO steps.
As a result, we can sample from the measure at each step of the gradient diffusion and compute its density.
arXiv Detail & Related papers (2021-06-01T19:21:48Z) - Wasserstein distance estimates for the distributions of numerical
approximations to ergodic stochastic differential equations [0.3553493344868413]
We study the Wasserstein distance between the in distribution of an ergodic differential equation and the distribution in the strongly log-concave case.
This allows us to study in a unified way a number of different approximations proposed in the literature for the overdamped and underdamped Langevin dynamics.
arXiv Detail & Related papers (2021-04-26T07:50:04Z) - Finite sample approximations of exact and entropic Wasserstein distances
between covariance operators and Gaussian processes [0.0]
We show that the Sinkhorn divergence between two centered Gaussian processes can be consistently and efficiently estimated.
For a fixed regularization parameter, the convergence rates are it dimension-independent and of the same order as those for the Hilbert-Schmidt distance.
If at least one of the RKHS is finite-dimensional, we obtain a it dimension-dependent sample complexity for the exact Wasserstein distance between the Gaussian processes.
arXiv Detail & Related papers (2021-04-26T06:57:14Z) - Optimal oracle inequalities for solving projected fixed-point equations [53.31620399640334]
We study methods that use a collection of random observations to compute approximate solutions by searching over a known low-dimensional subspace of the Hilbert space.
We show how our results precisely characterize the error of a class of temporal difference learning methods for the policy evaluation problem with linear function approximation.
arXiv Detail & Related papers (2020-12-09T20:19:32Z) - Faster Convergence of Stochastic Gradient Langevin Dynamics for
Non-Log-Concave Sampling [110.88857917726276]
We provide a new convergence analysis of gradient Langevin dynamics (SGLD) for sampling from a class of distributions that can be non-log-concave.
At the core of our approach is a novel conductance analysis of SGLD using an auxiliary time-reversible Markov Chain.
arXiv Detail & Related papers (2020-10-19T15:23:18Z) - On Projection Robust Optimal Transport: Sample Complexity and Model
Misspecification [101.0377583883137]
Projection robust (PR) OT seeks to maximize the OT cost between two measures by choosing a $k$-dimensional subspace onto which they can be projected.
Our first contribution is to establish several fundamental statistical properties of PR Wasserstein distances.
Next, we propose the integral PR Wasserstein (IPRW) distance as an alternative to the PRW distance, by averaging rather than optimizing on subspaces.
arXiv Detail & Related papers (2020-06-22T14:35:33Z) - Faster Wasserstein Distance Estimation with the Sinkhorn Divergence [0.0]
The squared Wasserstein distance is a quantity to compare probability distributions in a non-parametric setting.
In this work, we propose instead to estimate it with the Sinkhorn divergence.
We show that, for smooth densities, this estimator has a comparable sample complexity but allows higher regularization levels.
arXiv Detail & Related papers (2020-06-15T06:58:16Z)
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.