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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.

169,341 papers · 148 categories

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61123184245 · May 202619922001200920182026
48 results for averaging theorem

We analyze the question whether sliding window time averages applied to stationary increment processes converge to a limit in probability. The question centers on averages, correlations, and densities constructed via time averages of the increment x(t,T)=x(t+T)-x(t)and the assumption is that the increment is distribute…

2008-04-06abs ↗pdf ↗

Study Q-learning with averaging for reinforcement learning, proving efficient inference and error bounds.

problem Efficient inference and error bounds for Q-learning with averaging.
method Functional central limit theorem and asymptotic linear estimator for optimal Q-value function.
result Standardized partial-sum process converges weakly to a rescaled Brownian motion, matching instance-dependent lower bound for error.

We give a construction to obtain canonically an ``isotropic average'' of given C1C^1-close isotropic submanifolds of a symplectic manifold. To do so we use an improvement of Weinstein's submanifold averaging theorem (obtained in collaboration with H. Karcher) and apply ``Moser's trick''. We also present an application …

2002-08-27abs ↗pdf ↗

Using an elementary argument, we prove new fixed point theorems for classical elliptic complexes. We obtain new results for conformal relations and coisotropic intersections. We obtain theorems for the average intersections of families of special lagrangian and lagrangian varieties in certain homogeneous spaces.

2006-06-22abs ↗pdf ↗

Researchers develop a method to measure treatment effects in settings with shared states.

problem Measuring treatment effects in settings with shared states like prices, recommendations, or social signals.
method Double machine learning (DML) theorem with conditions for efficient inference under shared-state interference.
result Efficient estimation of average direct effect (ADE) and global average treatment effect (GATE) in various models.

This paper introduces sample-averaged Q-learning for better RL performance.

problem Improving reinforcement learning algorithms by managing uncertainty.
method Integrates statistical inference into Q-learning through sample averaging and functional central limit theorem.
result Establishes a unified theoretical foundation for sample-averaged Q-learning.

We describe an averaging procedure on a Dirac manifold, with respect to a class of compatible actions of a compact Lie group. Some averaging theorems on the existence of invariant realizations of Poisson structures around (singular) symplectic leaves are derived. We show that the construction of coupling Dirac structur…

2014-05-03abs ↗pdf ↗

Improves consistency of weighted averaging estimators in active learning.

problem Consistency issues in weighted averaging estimators under active learning.
method Enforces random sampling through an augmented active learning algorithm.
result Generalizes Stone's Theorem for consistency of estimators under active learning.

Enhanced Bishop-Gromov theorem for homogeneous and inhomogeneous spaces.

problem Bounding the volume growth of geodesic balls in spaces.
method Introducing coefficient shuffling and using the Raychaudhuri equation, geodesic flow conservation, and the full spectrum of Ricci curvature.
result Upper bounds on the average rate of growth of geodesics for finite-volume inhomogeneous spaces.

The study examines how average scalar curvature influences geometric properties of Riemannian manifolds.

problem Investigating the geometric properties of Riemannian manifolds influenced by average scalar curvature.
method Analyzing the conjugate radius, average area of geodesic spheres, average volume of metric balls, and total volume of closed manifolds.
result Improves the Bishop-Gromov estimate on the average volume of metric balls and proves monotone decreasing properties of certain geometric integrals.

Given a Finsler space (M,F) on a manifold M, the averaging method associates to Finslerian geometric objects affine geometric objects} living on MM. In particular, a Riemannian metric is associated to the fundamental tensor gg and an affine, torsion free connection is associated to the Chern-Rund connection. As an il…

2005-01-05abs ↗pdf ↗

Paper develops bounds for stochastic approximation with averaging.

problem Establish high-probability bounds for averaged stochastic approximation.
method Develops a general framework for non-asymptotic concentration bounds.
result Derives sharp bounds for averaged iterates and tightens existing results.

The standard Bonnet-Myers theorem says that if the Ricci scalar of a Riemannian manifold is bounded below by a positive number, then the manifold is compact. Moreover, a bound of its diameter is pointed out. The theorem was extended to Finsler manifolds. In this paper we prove that if a certain condition on the average…

2014-05-22abs ↗pdf ↗

Paper uses averaging from many particle filters to approximate posterior predictive distributions.

problem Approximating posterior predictive distributions efficiently and accurately.
method Particle swarm filter algorithm that averages many particle filter approximations.
result Law of large numbers and central limit theorem support the method's effectiveness.

Study on stochastic approximation with Polyak-Ruppert averaging for linear systems.

problem Understanding the asymptotic and non-asymptotic properties of stochastic approximation procedures.
method Detailed analysis of linear stochastic approximation with Polyak-Ruppert averaging, focusing on asymptotic and non-asymptotic properties.
result Proves CLT and non-asymptotic concentration inequality for averaged iterates, providing refined understanding of linear stochastic approximation.

New energy functional and fields for Yang-Mills theory, proving monotonicity and vanishing theorems.

problem Developing new mathematical tools for Yang-Mills theory.
method Introducing normalized exponential Yang-Mills energy functional, deriving monotonicity formula and vanishing theorem.
result Monotonicity and vanishing theorems for exponential Yang-Mills fields.

Paper improves CLT and bootstrap approximations for LSA with decreasing step size.

problem Improving normal approximation and bootstrap methods for LSA with decreasing step sizes.
method Refined Berry-Esseen bounds and multiplier bootstrap procedure for LSA.
result Approximation rates up to 1/n1/\sqrt{n} for LSA rescaled error distribution.

Lean Copilot uses LLMs to assist theorem proving in Lean, improving efficiency and automation.

problem Challenges in using existing neural theorem provers to prove novel theorems autonomously.
method Introduces Lean Copilot, a framework for integrating LLMs into Lean's theorem proving process.
result Lean Copilot automates 74.2% of proof steps on average, significantly improving over existing methods.

Paper finds optimal shapes for minimizing average lengths of billiard trajectories in specific polygons.

problem Finding optimal shapes to minimize the average length of billiard trajectories.
method Used techniques from Teichmüller theory.
result Optimal shapes minimize average lengths of billiard trajectories in specific polygons.

The study uses Ricci flow to prove flatness of certain Riemannian manifolds.

problem Proving the flatness of Riemannian manifolds with specific curvature properties.
method Ricci flow approach, quantitative existence theory, curvature estimates, and regularization.
result Manifolds with non-negative curvature and specific decay rates are necessarily flat.

Efficient inference method for adaptive experiments with tighter confidence sequences.

problem Efficient inference of Average Treatment Effect in a changing policy sequential experiment.
method Semiparametric efficient inference using Adaptive Augmented Inverse-Probability Weighted estimator and asymptotic confidence sequences.
result Derives tighter confidence sequences for adaptive experiments under data-dependent stopping times.

The paper establishes CLTs for Markov chains and improves sampling algorithms for heavy-tailed distributions.

problem Establishing central limit theorems for ergodic averages of Markov chains.
method Drift conditions to provide necessary and sufficient conditions for CLTs, including lower bounds on convergence rates.
result Sharp conditions and convergence rates for various MCMC algorithms on heavy-tailed targets.

New method for zeroth-order stochastic gradient algorithms provides confidence intervals.

problem Lack of inferential capabilities for zeroth-order stochastic gradient algorithms.
method Established central limit theorem and provided online estimators for asymptotic covariance matrix.
result Asymptotically valid confidence sets for parameter estimation and prediction.

In this paper a new connection between the discrete conformal geometry problem of disk pattern construction and the continuous conformal geometry problem of metric uniformization is presented. In a nutshell, we discuss how to construct disk patterns by optimizing an objective function, which turns out to be intimately …

2000-10-31abs ↗pdf ↗

In this note we prove the a pointwise ergodic theorem for functions taking values in a separable complete CAT(0)-space, analogous to Lindenstrauss' pointwise ergodic theorem for real-valued integrable functions on a probability space subject to a probability-preserving action of an amenable l.c.s.c. group, where in the…

2009-05-05abs ↗pdf ↗

The paper explores how dynamic preconditioning affects the CLT in online averaging.

problem When does dynamic preconditioning preserve the Polyak-Ruppert CLT?
method The authors decompose the averaged error and identify a stabilization-rate threshold for the CLT to hold.
result The CLT holds if the dynamic remainder vanishes in L2L^2 and the stabilization rate exceeds a threshold.

Stochastic gradient descent's long-term fluctuations are described by a diffusion limit.

problem Long-term behavior of stochastic gradient descent in non-smooth settings.
method Functional central limit theorem applied to rescaled trajectory of SGD.
result Characterization of long-term fluctuations around the minimizer.

The paper proves conditions for vanishing and estimating Betti numbers of Riemannian manifolds.

problem Vanishing and estimating Betti numbers of Riemannian manifolds with curvature constraints.
method Proves vanishing and estimation theorems for pextthp^{ ext{th}}-Betti numbers using curvature conditions.
result Vanishing of Betti numbers for 33-positive curvature operators in dimensions 5,65,6.

Sphere theorems proved for manifolds with specific curvature conditions.

problem Sphere theorems for Riemannian manifolds with curvature operator constraints.
method Investigation of eigenvalues and curvature operator conditions.
result Proved sphere theorems in dimensions three and four, homological sphere theorem in higher dimensions.

New algorithms for collaborative learning in uncertain, decentralized environments.

problem Byzantine collaborative learning in uncertain, decentralized environments.
method Two asynchronous solutions to averaging agreement, each optimal according to some dimension.
result New algorithms achieve optimal Byzantine resilience and collaborative learning.

In this paper, by applying a linear trace Li-Yau-Hamilton inequality for a positive (1,1)-form solution of the CR Hodge-Laplace heat equation and monotonicity of the heat equation deformation, we obtain an optimal gap theorem for a complete strictly pseudocovex CR manifold with nonnegative pseudohermitian bisectional c…

2015-04-03abs ↗pdf ↗