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arXiv research

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.

168,982 papers · 148 categories

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25.0%50.0%75.0%100.0% · Feb 199419922001200920172026
48 results for integral gradient estimate

Gradient and Laplacian estimates for complex Monge-Ampère equations found.

problem Estimating solutions to complex Monge-Ampère equations with singularities.
method Integral method applied to obtain gradient and Laplacian estimates.
result Gradient and Laplacian estimates for the solution to the singular complex Monge-Ampère equation.

The paper provides gradient estimates for solutions on manifolds with integral Ricci bounds.

problem Global regularity estimates for solutions of Δu=fΔu = f on Riemannian manifolds.
method Proves LpL^p-gradient estimates under integral Ricci bounds and constructs a counterexample.
result Optimal constant lower bounds on Ricci curvature are shown in the pointwise sense.

The paper proves gradient estimates for a weighted p-Laplacian equation on Riemannian manifolds.

problem Gradient estimates for a weighted p-Laplacian equation on Riemannian manifolds.
method Assumes a Sobolev inequality and integral Ricci bounds, proving local gradient estimates and Liouville type results.
result Proves local gradient estimates and Liouville type results on manifolds with lower bounds of Ricci curvature.

Estimates gradients of solutions on closed surfaces.

problem Gradient estimates for solutions on closed surfaces.
method Considered a new metric g=e2ugg' = e^{2u} g with bounded integral curvature, derived gradient estimates for gg', and used these to obtain gradient estimates for uu.
result Gradient estimates for solutions on closed surfaces are established.

The paper derives new gradient estimates for nonlinear elliptic equations under integral Ricci curvature bounds.

problem Gradient estimates for nonlinear elliptic equations under integral Ricci curvature bounds.
method Moser's iteration method applied to positive solutions.
result New local and global gradient estimates for positive solutions are derived.

Study on gradient h-almost Yamabe solitons with scalar curvature estimation.

problem Exploring triviality and scalar curvature estimation of gradient h-almost Yamabe solitons.
method Established sufficient conditions for triviality and scalar curvature estimation under integral inequalities involving the scalar curvature and soliton function.
result Extended and refined former works on almost and h-almost Yamabe solitons, characterizing their geometric structures.

We obtain a local Sobolev constant estimate for integral Ricci curvature, which enables us to extend several important tools such as the maximal principle, the gradient estimate, the heat kernel estimate and the L2L^2 Hessian estimate to manifolds with integral Ricci lower bounds, without the non-collapsing conditions.

2016-01-29abs ↗pdf ↗

A new method for estimating uncertainties in neural ODEs without numerical integration.

problem Accurate estimation of predictive uncertainties in neural ODEs.
method Distributional Gradient Matching (DGM) algorithm that jointly trains a smoother and a dynamics model.
result Significantly more accurate predictions compared to traditional methods.

New estimator for SDEs is shown to be an adjoint state method.

problem Estimating gradients for overparameterized SDEs efficiently.
method Demonstrates generator gradient estimator as an adjoint state method.
result Generator gradient estimator is an adjoint state method for SDEs.

New estimators reduce variance in training variational autoencoders with discrete latent variables.

problem Training variational autoencoders with discrete latent variables requires efficient gradient estimation.
method Introduce ReinMax-Rao and ReinMax-CV estimators using Rao-Blackwellisation and control variates.
result Demonstrate superior performance on training variational autoencoders with discrete latent spaces.

The paper establishes Harnack inequalities for solutions of nonlinear parabolic equations on manifolds with integral Ricci curvature bounds.

problem Analyzing solutions of nonlinear parabolic equations on manifolds with specific curvature constraints.
method Establishing space-time gradient estimates and integrating them to find Harnack inequalities.
result Harnack inequalities for positive solutions of nonlinear parabolic equations under integral Ricci curvature bounds.

Improved heat equation estimates without gradient curvature assumption.

problem Improving Hamilton's matrix Harnack estimate for heat equation without gradient curvature assumption.
method New ingredients include a sharp Li-Yau estimate, a suitable vector field construction, and integral arguments.
result Removed the gradient curvature assumption in Hamilton's estimate for heat equation.

The gradient shrinking ρρ-Einstein soliton is a triple (Mn,g,f)(M^n,g,f) such that Rij+fij=(ρR+λ)gij,R_{ij}+f_{ij}=(ρR+λ) g_{ij}, where (Mn,g)(M^n,g) is a Riemannian manifold, λ>0,ρR{0}λ>0, ρ\in\mathbb{R}\setminus\{0\} and ff is the potential function on MnM^n. In this paper, using algebraic curvature estimates and the Yamabe-Sobolev inequality, w…

2016-12-27abs ↗pdf ↗

The paper proves geometric comparisons on metric measure spaces with integral Bakry-Émery Ricci tensor bounds.

problem Geometric comparisons on metric measure spaces with specific tensor bounds.
method Integral radial Bakry-Émery Ricci tensor bounds and potential function/gradient bounds.
result Diameter and eigenvalue estimates on smooth metric measure spaces.

The paper establishes sub-gradient estimates and entropy formulas for quaternionic contact geometry heat equations.

problem Developing sub-gradient estimates and entropy formulas for quaternionic contact geometry.
method Establishing sub-gradient estimates and entropy formulas for the quaternionic contact heat equation.
result Two Perelman-type entropy formulas and sub-gradient estimates for the quaternionic contact heat equation.

The paper estimates gradients for a weighted parabolic equation under geometric flow.

problem Estimating gradients for a specific parabolic equation on a weighted manifold.
method Obtained space-time gradient estimates through integrating the equation.
result Found corresponding Harnack inequalities through gradient estimates.

We study pointwise and LpL^p gradient estimates of the heat kernel, on manifolds that may have some amount of negative Ricci curvature, provided it is not too negative (in an integral sense) at infinity. We also prove uniform boundedness results on LpL^p spaces for the heat operator of the Hodge Laplacian on differenti…

2018-03-27abs ↗pdf ↗

The paper examines partial regularity of Lipschitz solutions to minimal surface system.

problem Understanding the regularity of solutions to the minimal surface system.
method Investigation of stationary, integral weak, and viscosity solutions; interior gradient estimate using maximum principle.
result Partial regularity results for Lipschitz solutions, including interior gradient estimate.

Study geometric and topological properties of Finsler manifolds with weighted Ricci curvature bounds.

problem Geometric and topological properties of Finsler metric measure manifolds with integral weighted Ricci curvature bounds.
method Establish Laplacian comparison theorem, volume comparison theorems, volume growth estimate, Gromov pre-compactness, local Dirichlet isoperimetric constant estimate.
result First Dirichlet eigenvalue estimate and gradient estimate for harmonic functions.

We prove a Li-Yau gradient estimate for positive solutions to the heat equation, with Neumann boundary conditions, on a compact Riemannian submanifold with boundary MnNn{\bf M}^n\subseteq {\bf N}^n, satisfying the integral Ricci curvature assumption: \begin{equation} D^2 \sup_{x\in {\bf N}} \left( \oint_{B(x,D)} |Ric^-|^…

2017-10-24abs ↗pdf ↗

A quantum reinforcement learning algorithm reduces sample complexity.

problem Quantum reinforcement learning under model-free settings with quantum oracle access.
method Quantum Natural Policy Gradient (QNPG) algorithm replacing random sampling with deterministic gradient estimation.
result QNPG achieves a sample complexity of ildeO(ε1.5) ilde{\mathcal{O}}(ε^{-1.5}) for queries to the quantum oracle, significantly improving classical lower bound.

Quantitative Sobolev extensions lead to Neumann heat kernel bounds.

problem Bounding Neumann heat kernels for domains with integral Ricci curvature.
method Quantitative Sobolev extension operators and Neumann heat kernel estimates.
result Uniform bounds on Neumann heat kernels and eigenvalues.

The paper examines LpL^p gradient and Riesz transform estimates under Ricci lower bounds.

problem Investigating LpL^p estimates for solutions of the Poisson equation under Ricci lower bounds.
method Analyzes LpL^p estimates for gradient and Riesz transforms under Ricci lower bounds, providing counterexamples and bounds.
result Valid LpL^p estimates for gradient and Riesz transforms under Ricci lower bounds, with conditions on injectivity radius and curvature.

Twin-Boot integrates uncertainty estimation into optimization using parallel training of identical models.

problem Uncertainty in overparameterized models, especially in low-data regimes.
method Twin-Bootstrap Gradient Descent (Twin-Boot) trains two identical models on independent bootstrap samples and uses their divergence to guide learning.
result Improves calibration and generalization, yields interpretable uncertainty maps.

Minimal graphs grow slowly on curved spaces, proving constant solutions.

problem Characterizing minimal graphs with sublinear growth on manifolds.
method New technique to get gradient bounds by integral estimates, no further geometric assumptions.
result Entire solutions are constant when negative part grows like r/logrr/\log r.

Regression trees learn gradients of differentiable functions.

problem Understanding gradients of differentiable functions using regression trees.
method Developed a method to estimate gradients of differentiable functions using regression trees and exposed quantities from tree learning libraries.
result Gradient estimates from regression trees can be used to improve predictive analysis and solve tasks in uncertainty quantification.

The paper characterizes solitons and estimates scalar curvature.

problem Characterizing and estimating scalar curvature of generalized Ricci-Yamabe solitons.
method Characterization and estimation of scalar curvature through soliton properties.
result Conditions for scalar curvature to be constant and estimation of Ricci curvature.

The paper studies gradient estimates and monotonicity of parabolic frequency for solutions to the Laplacian G_2 flow.

problem Gradient estimates and monotonicity of parabolic frequency for solutions to the Laplacian G_2 flow.
method Gradient estimates and Harnack inequalities for heat equations under the Laplacian G_2 flow.
result Monotonicity of parabolic frequency and backward uniqueness for positive solutions.

We prove that a nn-dimensional, 4n64 \leq n \leq 6, compact gradient shrinking Ricci soliton satisfying a Ln/2L^{n/2}-pinching condition is isometric to a quotient of the round Sn\mathbb{S}^{n}. The proof relies mainly on sharp algebraic curvature estimates, the Yamabe-Sobolev inequality and an improved rigidity result f…

2015-09-24abs ↗pdf ↗

Research on manifold learning within a density ridge estimation framework has shown great potential in recent work for both estimation and de-noising of manifolds, building on the intuitive and well-defined notion of principal curves and surfaces. However, the problem of unwrapping or unfolding manifolds has received r…

2016-04-06abs ↗pdf ↗

Fenrir uses probabilistic numerics to simplify solving initial value problems.

problem Solving initial value problems in ordinary differential equations.
method Probabilistic numerics and Gauss--Markov regression.
result The method simplifies parameter estimation in ODEs, making it easier and more robust.

We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected sarsa, EPG integrates across the action when estimating the gradient, instead of relying only on the action in the sampled trajectory. We es…

2017-06-15abs ↗pdf ↗

Unbiased gradient estimation improves VAE performance.

problem Training VAEs via maximum likelihood is difficult due to intractable integrals.
method Introduced unbiased estimators of the log-likelihood gradient using coupled Markov chains.
result Unbiased estimators lead to better predictive performance in VAEs.

In the first part of the paper we derive integral curvature estimates for complete gradient shrinking Ricci solitons. Our results and the recent work of Lopez-Rio imply rigidity of gradient shrinking Ricci solitons with harmonic Weyl tensor. In the second part of the paper we address the issue of existence of harmonic …

2009-10-06abs ↗pdf ↗