A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper proposes approximate Stein classes for efficient truncated density estimation.
Stochastic Stein Discrepancies improve inference efficiency.
New Stein operator improves robustness in model inference.
New samplers minimize KL divergence for constrained and non-Euclidean geometries.
Stein's method for measuring convergence to a continuous target distribution relies on an operator characterizing the target and Stein factor bounds on the solutions of an associated differential equation. While such operators and bounds are readily available for a diversity of univariate targets, few multivariate targ…
Bayesian neural networks speed up numerical integration.
An important task in machine learning and statistics is the approximation of a probability measure by an empirical measure supported on a discrete point set. Stein Points are a class of algorithms for this task, which proceed by sequentially minimising a Stein discrepancy between the empirical measure and the target an…
Ad-SVGD optimizes kernel parameters for SVGD, improving inference performance.
Study approximates weak error for specific stochastic models with rough and Gaussian mean-reverting volatility.
Improved neural network ensembles using Stein Variational Newton updates.
An important task in computational statistics and machine learning is to approximate a posterior distribution with an empirical measure supported on a set of representative points . This paper focuses on methods where the selection of points is essentially deterministic, with an emphasis on achi…
Regularized Stein thinning improves MCMC output approximations.
AgraSSt assesses graph generators using Stein operators and kernel discrepancies.
Computable Stein discrepancies have been deployed for a variety of applications, ranging from sampler selection in posterior inference to approximate Bayesian inference to goodness-of-fit testing. Existing convergence-determining Stein discrepancies admit strong theoretical guarantees but suffer from a computational co…
New method uses kernel Stein discrepancy for measure transport without strict continuity constraints.
Stein transport improves Bayesian inference with faster convergence and reduced variance.
Improving scalability and stability of Stein discrepancies for scalable goodness-of-fit testing
SRF improves kernel approximation and GP regression performance.
The paper analyzes rates for a modified gradient descent method using Stein variational gradients.
New open books solve a long-standing surface mapping class group question.
Improved sampling method using regularized Stein Variational Gradient Flow.
Approximate Markov chain Monte Carlo (MCMC) offers the promise of more rapid sampling at the cost of more biased inference. Since standard MCMC diagnostics fail to detect these biases, researchers have developed computable Stein discrepancy measures that provably determine the convergence of a sample to its target dist…
Stein variational gradient descent (SVGD) is a deterministic sampling algorithm that iteratively transports a set of particles to approximate given distributions, based on an efficient gradient-based update that guarantees to optimally decrease the KL divergence within a function space. This paper develops the first th…
CSD improves goodness-of-fit testing for higher-order dependence.
New KSDs control moments in approximations, improving diagnostics and tests.
We characterize the closed, oriented, Seifert fibered 3-manifolds which are oriented boundaries of Stein manifolds. We also show that for this class of 3-manifolds the existence of Stein fillings is equivalent to the existence of symplectic fillings.
New method uses multiple kernels to improve SVGD performance.
A new method improves inference for complex Bayesian models.
We use contact handle decompositions and a stabilization process to compute the cylindrical contact homology of a subcritical Stein-fillable contact manifold with vanishing first Chern class, and show that it is completely determined by the homology of a subcritical Stein-filling of the contact manifold.
We give an algorithm which produces infinitely many pairwise exotic Stein fillings of the same contact 3-manifolds, applying positive allowable Lefschetz fibrations over the disk. As a corollary, for a large class of Stein fillings, we realize the topological invariants (i.e. fundamental group, homology group, homology…
SDG uses optimal control to improve classifier guidance in low-density regions.
Study Stein and Milnor fillings of links from surface singularities.
A new framework improves kernel Stein discrepancy tests for validating distributions.
SVGD algorithm converges at rate 1/sqrt(log log n) for sub-Gaussian distributions.
The purpose of this paper is to synthesize the approaches taken by Chatterjee-Meckes and Reinert-Röllin in adapting Stein's method of exchangeable pairs for multivariate normal approximation. The more general linear regression condition of Reinert-Röllin allows for wider applicability of the method, while the method of…
KSD Descent uses KSD to sample from a target distribution efficiently.
New algorithm tunes SGMCMC hyperparameters for scalable Bayesian inference.
Paper analyzes SVGD algorithm for non-asymptotic convergence.
Stein's method (Stein, 1973; 1981) is a powerful tool for statistical applications and has significantly impacted machine learning. Stein's lemma plays an essential role in Stein's method. Previous applications of Stein's lemma either required strong technical assumptions or were limited to Gaussian distributions with …
We develop Riemannian Stein Variational Gradient Descent (RSVGD), a Bayesian inference method that generalizes Stein Variational Gradient Descent (SVGD) to Riemann manifold. The benefits are two-folds: (i) for inference tasks in Euclidean spaces, RSVGD has the advantage over SVGD of utilizing information geometry, and …
New Stein identity for q-Gaussians reduces gradient variance in machine learning.
Bayesian inference problems require sampling or approximating high-dimensional probability distributions. The focus of this paper is on the recently introduced Stein variational gradient descent methodology, a class of algorithms that rely on iterated steepest descent steps with respect to a reproducing kernel Hilbert …
New Stein fillings found for rational surface singularities.
We introduce a new generalization of Gompf nuclei and give applications. We construct infinitely many exotic smooth structures for a large class of compact 4-manifolds with boundary, regarding topological invariants. We prove that a large class of closed 3-manifolds (including disjoint unions of Stein fillable 3-manifo…
Stein variational gradient descent (SVGD) is a non-parametric inference algorithm that evolves a set of particles to fit a given distribution of interest. We analyze the non-asymptotic properties of SVGD, showing that there exists a set of functions, which we call the Stein matching set, whose expectations are exactly …
Stein importance sampling is a widely applicable technique based on kernelized Stein discrepancy, which corrects the output of approximate sampling algorithms by reweighting the empirical distribution of the samples. A general analysis of this technique is conducted for the previously unconsidered setting where samples…
In this article, we prove a generalization of a theorem of Lisca-Matic to Stein cobordisms and develop a method for distinguishing certain Stein cobordisms using rotation numbers. Using these results along with standard techniques from convex surface theory and classifications of tight contact structures on certain 3-m…
In this article, using combinatorial techniques of mapping class groups, we show that a Stein fillable integral homology -sphere supported by an open book decomposition with page a -holed sphere admits a unique Stein filling up to diffeomorphism. Furthermore, according to a property of deforming symplectic fillin…