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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.

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54109163217 · Jun 202019922001200920172026
48 results for Riesz Regression

Riesz regression connects to density ratio estimation for causal inference.

problem Estimating average treatment effects in causal inference.
method Riesz regression as a signed density ratio and least-squares importance fitting.
result Riesz regression and DRE are equivalent, allowing transfer of DRE results.

Develops a direct debiased machine learning framework using Bregman divergence.

problem Reduces bias in machine learning estimates of causal effects or structural models.
method Neyman targeted estimation and generalized Riesz regression using Bregman divergence.
result Improves estimation of parameters of interest in causal models.

Python package automates causal parameter estimation using Riesz regression.

problem Efficient estimation of causal and structural parameters.
method Automatic DML and generalized Riesz regression framework.
result Automatic construction of balancing link functions for generalized Riesz regression.

Unified framework for debiased machine learning using Riesz representer and Bregman divergence.

problem Estimating causal and structural parameters in machine learning.
method Generalized Riesz regression for fitting Riesz representer via Bregman divergence minimization.
result Automatic covariate balancing and Neyman orthogonality properties for debiased estimation.

Unified framework for estimating density ratios in causal inference.

problem Estimating density ratios for causal inference is challenging due to instability and curse of dimensionality.
method Bregman-Riesz regression unifies three methods: Bregman divergences, probabilistic classification, and Riesz loss.
result Unified framework improves density ratio estimation in causal inference.

Gradient boosting estimates Riesz representer for causal inference.

problem Estimating causal quantities using traditional methods is challenging and prone to variance issues.
method Gradient boosting algorithm to directly estimate Riesz representer.
result Gradient boosting performs similarly or better than traditional methods in estimating causal quantities.

Prediction-powered causal inference achieves smaller asymptotic variance than traditional methods.

problem Estimating causal and structural parameters in a semi-supervised setting.
method Combining efficient influence function with debiased machine learning and semi-supervised Riesz regression.
result Asymptotic variances of estimators match the derived efficiency bound.

The paper argues for using Neyman orthogonal score for balancing in debiased machine learning.

problem Debiased machine learning requires a proper approach to balance covariates.
method The paper advocates for using Riesz regression with basis functions of X for balancing.
result Covariate balancing is only valid when the score-relevant regression error is a function of covariates alone.

Improved AutoDML estimator for causal inference using outcome-adapted shared covariate representation.

problem Efficiency in estimating treatment or policy effects in causal inference.
method Outcome-adapted AutoDML estimator that uses a shared covariate representation that is predictive of the outcome but not the Riesz representer.
result Outcome-adapted AutoDML estimator is asymptotically more efficient than baseline AutoDML.

Kernel balancing weights are generalized as KRRR, providing better confidence intervals for treatment effects.

problem Lack of generalization error, correct feature specification, and limited to average effects.
method Interpreting kernel balancing weights as KRRR, relaxing feature specification, and extending Gaussian approximation.
result KRRR provides strong generalization properties and justifies confidence sets for causal functions.

We show a perturbation result for the boundedness of the Riesz transform : if MM and M0M_0 are complete Riemannian manifolds satisfying a Sobolev inequality of dimension nn, which are isometric outside a compact set, and if the Riesz transform on M0M_0 is bounded on LqL^q, then for all $\frac{n}{n-2}, the Riesz trans…

2011-05-30abs ↗pdf ↗

In this paper we prove mixed norm estimates for Riesz transforms related to Laplace--Beltrami operators on compact Riemannian symmetric spaces of rank one. These operators are closely related to the Riesz transforms for Jacobi polynomials expansions. The key point is to obtain sharp estimates for the kernel of the Jaco…

2013-08-29abs ↗pdf ↗

We investigate the LpL^p-boundness of the Riesz transform on Riemannian manifolds whose Ricci curvature has quadratic decay. Two criteria for the LpL^p-unboundness of the Riesz transform are given. We recover known results about manifolds that are Euclidean or conical at infinity.

2014-03-25abs ↗pdf ↗

Study LpL^p boundedness of Riesz transform on differential forms for certain manifolds.

problem Investigate LpL^p-boundedness of the covariant Riesz transform on differential forms.
method Analyze LpL^p-boundedness on weighted Riemannian manifolds under curvature-dimension and lower bound conditions.
result Derive Calderón-Zygmund inequality for 1<p21<p\leq2 under curvature-dimension condition.

Let MM be a complete non-compact Riemannian manifold. In this paper, we derive sufficient conditions on metric perturbation for stability of LpL^p-boundedness of the Riesz transform, p(2,)p\in (2,\infty). We also provide counter-examples regarding in-stability for LpL^p-boundedness of Riesz transform.

2018-08-06abs ↗pdf ↗

Two approaches to directly estimating Riesz representer are shown to be numerically equivalent under certain conditions.

problem Estimating Riesz representer in semiparametric statistics.
method Two distinct optimization problems solved by automatic debiased machine learning and sieve methods for conditional moment models.
result Numerical equivalence of estimators under specific regularization schemes, but not for others.

The study bounds Riesz transforms on manifolds with controlled curvature.

problem Bounding Riesz transforms on manifolds with controlled curvature.
method Established LpL^p-boundedness of local covariant Riesz transforms for differential forms.
result Calderón-Zygmund estimates for manifolds with bounded Riemannian curvature.

Automatic debiasing for causal and policy effects using Neural Nets and Random Forests.

problem Estimating causal and policy effects from high-dimensional or non-parametric regression functions.
method Automatic learning of Riesz representation using Neural Nets and Random Forests.
result Automatic debiasing method performs well compared to state-of-the-art algorithms.

We show that balls, circles and 2-spheres can be identified by generalized Riesz energy among compact submanifolds of the Euclidean space that are either closed or with codimension 0, where the Riesz energy is defined as the double integral of some power of the distance between pairs of points. As a consequence, we obt…

2017-07-08abs ↗pdf ↗

ScoreMatchingRiesz improves debiased machine learning and policy effects estimation.

problem Improving debiased machine learning and policy effects estimation.
method Score matching and Riesz representer estimation.
result Estimates policy path for continuous treatments, improving interpretability.

The paper proves boundedness of a Riesz transform on weighted manifolds.

problem Establishing \(L^p\)-boundedness of the covariant Riesz transform on differential forms.
method Heat-kernel criterion, volume doubling, heat kernel estimates, curvature control, gradient bounds.
result The covariant Riesz transform is \(L^p\)-bounded for \(p>2\) on weighted Riemannian manifolds.

Let MM be a smooth Riemannian manifold which is the union of a compact part and a finite number of Euclidean ends, $\RR^n \setminus B(0,R)$ for some R>0R > 0, each of which carries the standard metric. Our main result is that the Riesz transform on MM is bounded from Lp(M)Lp(M;TM)L^p(M) \to L^p(M; T^*M) for 1<p<n1 < p < n and unbou…

2004-11-30abs ↗pdf ↗

DPI quantifies phase differences in 1D and multidimensional signals using Riesz transform.

problem Quantifying phase differences in signals of varying dimensions.
method Riesz transform framework for harmonic analysis.
result DPI detects hypersynchronization and subtle changes in images and artworks.

Local Hardy spaces defined for Riemannian manifolds with bounded geometry.

problem Defining Hardy spaces for Riemannian manifolds with specific curvature conditions.
method Using local Riesz transforms and atomic Goldberg-type spaces.
result Atomic Hardy spaces and local Hardy spaces are equivalent on Riemannian manifolds with bounded geometry.

Bayesian Additive Distribution Regression (DistBART) predicts distributions from grouped data.

problem Predicting distributions from grouped data with varying characteristics.
method Bayesian nonparametric approach using BART for modeling the regression function.
result Empirical and theoretical evidence supports DistBART's effectiveness in learning from low-dimensional marginals.

Let (M,g)(M^\circ, g) be an asymptotically conic manifold, in the sense that MM^\circ compactifies to a manifold with boundary MM in such a way that gg becomes a scattering metric on MM. A special case of particular interest is that of asymptotically Euclidean manifolds, where M=Sn1\partial M = S^{n-1} and the induced me…

2007-03-12abs ↗pdf ↗

New method for estimating treatment effects without complex propensity models.

problem Estimating treatment effects in dynamic treatment regimes.
method Recursive Riesz representer estimation for de-biasing corrections.
result Directly estimates de-biasing corrections without auxiliary models.

In this paper we study the problem of deriving further Sobolev inequalities from a given Sobolev inequality. We use several different methods, including Bessel potentials and Riesz transforms. We apply the results to the Ricci flow to extend the author's results on the W1,2W^{1,2} Sobolev inequality along the Ricci flow …

2007-09-04abs ↗pdf ↗

One considers the class of complete non-compact Riemannian manifolds whose heat kernel satisfies Gaussian estimates from above and below. One shows that the Riesz transform is LpL^p bounded on such a manifold, for pp ranging in an open interval above 2, if and only if the gradient of the heat kernel satisfies a certai…

2004-11-17abs ↗pdf ↗

In this paper, we show the equivalence between the boundedness of the Riesz transform dΔ1/2dΔ^{-1/2} on LpL^p, p(2,p0)p\in (2,p_0), and the equality Hp=LpH^p=L^p, p(2,p0)p\in(2,p_0), in the class of manifold whose measure is doubling and for which the scaled Poincaré inequalities hold. Here, HpH^p is a Hardy space of exact 11-forms, …

2013-08-27abs ↗pdf ↗

In this paper we introduce a new family of operator-valued distributions on Euclidian space acting by convolution on differential forms. It provides a natural generalization of the important Riesz distributions acting on functions, where the corresponding operators are (Δ)α/2(-Δ)^{-α/2}, and we develop basic analogous prop…

2017-02-03abs ↗pdf ↗

The goal of this article is twofold: in a first part, we prove Gaussian estimates for the heat kernel of Schr{ö}dinger operators delta + V whose potential V is "small at infinity" in an integral sense. In a second part, we prove sharp boundedness result for the associated Riesz transform with potential d(delta+V) --1/2…

2015-03-02abs ↗pdf ↗