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.
New Stein identity for q-Gaussians reduces gradient variance in machine learning.
Lower bounds on private estimation of Gaussian covariance matrices.
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 construct a contact 5-manifold supported by infinitely many distinct open books with the identity monodromy and pairwise exotic Stein pages (i.e. pages are pairwise homeomorphic but non-diffeomorphic Stein fillings of a fixed contact 3-manifold), moreover we describe a process of generating infinitely many such exam…
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…
Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems. However, it still often suffers from the large variance issue on policy gradient estimation, which leads to poor sample efficiency during training. In this work, we propose a control variate method to effe…
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 …
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 …
We consider estimating the parametric components of semi-parametric multiple index models in a high-dimensional and non-Gaussian setting. Such models form a rich class of non-linear models with applications to signal processing, machine learning and statistics. Our estimators leverage the score function based first and…
We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization. Our method iteratively transports a set of particles to match the target distribution, by applying a form of functional gradient descent that minimizes the KL divergence. Empirical studies…
A new filter reduces density fitting to a linear solve, improving performance on nonlinear systems.
Paper proposes approximate Stein classes for efficient truncated density estimation.
We derive a new discrepancy statistic for measuring differences between two probability distributions based on combining Stein's identity with the reproducing kernel Hilbert space theory. We apply our result to test how well a probabilistic model fits a set of observations, and derive a new class of powerful goodness-o…
We study the parameter estimation problem for a varying index coefficient model in high dimensions. Unlike the most existing works that iteratively estimate the parameters and link functions, based on the generalized Stein's identity, we propose computationally efficient estimators for the high-dimensional parameters w…
Efficiently samples and learns densities with symmetries using equivariant methods.
Stein-Encoder isolates genetic signals in multi-modal biomedical data.
We study the estimation of the parametric components of single and multiple index volatility models. Using the first- and second-order Stein's identities, we develop methods that are applicable for the estimation of the variance index in the high-dimensional setting requiring finite moment condition, which allows for h…
New method reduces variance in Bayesian inverse problems.
A new neural network initialization method is proposed for faster and more accurate training.
New formulae identify discrete probability laws without needing normalization constants.
We consider a fixed contact 3-manifold that admits infinitely many compact Stein fillings which are all homeomorphic but pairwise non-diffeomorphic. Each of these fillings gives rise to a closed contact 5-manifold described as a contact open book whose page is the filling at hand and whose monodromy is the identity sym…
In this paper, we propose and analyze zeroth-order stochastic approximation algorithms for nonconvex and convex optimization, with a focus on addressing constrained optimization, high-dimensional setting and saddle-point avoiding. To handle constrained optimization, we first propose generalizations of the conditional g…
Stein's method improves probabilistic inference and learning.
Unified score and distance-based GoF tests for model adequacy.
GC Stein manifolds characterized with embeddings and functions.
Stochastic Stein Discrepancies improve inference efficiency.
We give a bordism-theoretic characterisation of those closed almost contact (2q+1)-manifolds (with q > 2) which admit a Stein fillable contact structure. Our method is to apply Eliashberg's h-principle for Stein manifolds in the setting of Kreck's modified surgery. As an application, we show that any simply connected a…
Improved sampling method using regularized Stein Variational Gradient Flow.
Develops Stein's method for Riemannian manifolds using diffusion.
It is shown that every subcritical Stein manifold is deformation equivalent to the product of a Stein manifold with $\C$.
Regularized Stein thinning improves MCMC output approximations.
Study Stein and Milnor fillings of links from surface singularities.
Stein transport improves Bayesian inference with faster convergence and reduced variance.
In this paper we survey results on the existence of holomorphic embeddings and immersions of Stein manifolds into complex manifolds. Most results pertain to proper maps into Stein manifolds. We include a new result saying that every continuous map between Stein manifolds is homotopic to a proper holomorphic em…
Counterexample found for Stein property of certain solvable Lie groups.
A new framework improves kernel Stein discrepancy tests for validating distributions.
2-stein submanifolds in space forms have constant curvature if normal connection is flat or codimension is 2.
We show that, under a certain condition, contact 5-manifolds can `coarsely' distinguish smooth structures on compact Stein 4-manifolds via contact open books. We also give a simple sufficient condition for an infinite family of Stein 4-manifolds to have an infinite subfamily of pairwise non-diffeomorphic Stein 4-manifo…
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…
Sliced kernelized Stein discrepancy improves goodness-of-fit tests and model learning in high dimensions.
For any integer , we construct an infinite family of Stein fillable contact -manifolds each of which admits infinitely many pairwise homotopy inequivalent Stein fillings.
The square root of Fredholm determinants causes numerical instabilities in option pricing models.
Study Stein fillings of planar contact 3-manifolds with relative trisection genus 2.
Study continuity and Hölder estimates for solutions on Stein spaces.
Improving scalability and stability of Stein discrepancies for scalable goodness-of-fit testing
Much of machine learning relies on comparing distributions with discrepancy measures. Stein's method creates discrepancy measures between two distributions that require only the unnormalized density of one and samples from the other. Stein discrepancies can be combined with kernels to define kernelized Stein discrepanc…
Paper proves existence of compatible Lefschetz fibrations on 6-ball and Stein domains.
New Stein operator improves robustness in model inference.