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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.

168,695 papers · 148 categories

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65130194259 · Jun 202019922001200920172026
48 results for uniformly absolutely continuous

The nearest neighbor rule is proven consistent in a broad setting.

problem Proving consistency of the nearest neighbor rule in various settings.
method Proving online consistency for all measurable functions in doubling metric spaces under mild assumptions.
result The nearest neighbor rule is online consistent in all measurable functions in doubling metric spaces.

The paper studies curves in Finsler-like spaces and their properties.

problem Investigating properties of curves in asymmetric metric spaces induced by Finsler structures.
method Analyzes three types of absolutely continuous curves in Finsler-like spaces and establishes the Lisini structure theorem.
result Characterizes the nature of absolutely continuous curves in terms of dynamical transference plans.

Study absolute continuity of Wasserstein barycenters on manifolds with singular cost functions.

problem Absolute continuity of Wasserstein barycenters on manifolds with singular cost functions.
method Approximation framework to handle singularity, geometrically transparent.
result Precise analytic condition on cost profile for necessary assumptions.

New method proves absolute continuity of Wasserstein barycenters on manifolds with lower Ricci curvature bound.

problem Proving absolute continuity of Wasserstein barycenters on manifolds with lower Ricci curvature bound.
method Introducing new displacement functionals exploiting Hessian equality and revisiting Souslin space theory, Dunford-Pettis theorem, and de la Vallée Poussin criterion for uniform integrability.
result Absolute continuity of Wasserstein barycenters is established for a general class of manifolds with lower Ricci curvature bound.

Monotone aggregation of dependent random vectors has an absolutely continuous distribution under certain conditions.

problem Monotone aggregation of dependent random vectors
method Coordinatewise monotonicity and uniform lower-increment conditions
result One-dimensional push-forwards of dependent random vectors have an absolutely continuous distribution

We study the stability of several no-arbitrage conditions with respect to absolutely continuous, but not necessarily equivalent, changes of measure. We first consider models based on continuous semimartingales and show that no-arbitrage conditions weaker than NA and NFLVR are always stable. Then, in the context of gene…

2013-12-16abs ↗pdf ↗

The folk result in Kyle-Back models states that the value function of the insider remains unchanged when her admissible strategies are restricted to absolutely continuous ones. In this paper we show that, for a large class of pricing rules used in current literature, the value function of the insider can be finite when…

2018-12-18abs ↗pdf ↗

This paper examines limits of Riemannian 2-manifolds with bounded curvature.

problem Understanding the limits of Riemannian 2-manifolds with bounded curvature.
method Uniform semi-locally 1-connected sequences of closed connected Riemannian 2-manifolds with bounded total absolute curvature.
result Description of Gromov-Hausdorff limits of the sequences.

Three themes of general topology: quotient spaces; absolute retracts; and inverse limits - are reapproached here in the setting of metrizable uniform spaces, with an eye to applications in geometric and algebraic topology. The results include: 1) If f: A -> Y is a uniformly continuous map, where X and Y are metric spac…

2011-06-16abs ↗pdf ↗

The intensity of a default time is obtained by assuming that the default indicator process has an absolutely continuous compensator. Here we drop the assumption of absolute continuity with respect to the Lebesgue measure and only assume that the compensator is absolutely continuous with respect to a general σσ-finite …

2015-12-12abs ↗pdf ↗

There is a well-known correspondence between infinite trees and ultrametric spaces which can be interpreted as an equivalence of categories and comes from considering the end space of the tree. In this equivalence, uniformly continuous maps between the end spaces are translated to some classes of coarse maps (or even c…

2007-04-24abs ↗pdf ↗

In a recent joint work with V. Turaev (cf. math.DG/9810114) we defined a new concept of combinatorial torsion which we called absolute torsion. Compared with the classical Reidemeister torsion it has the advantage of having a well-defined sign. Also, the absolute torsion is defined for arbitrary orientable flat vector …

1999-03-23abs ↗pdf ↗

In this paper we prove the probabilistic continuous complexity conjecture. In continuous complexity theory, this states that the complexity of solving a continuous problem with probability approaching 1 converges (in this limit) to the complexity of solving the same problem in its worst case. We prove the conjecture ho…

2012-12-06abs ↗pdf ↗

Given a definable function f, enough differentiable, we study the continuity of the total curvature function t --> K(t), total curvature of the level {f=t}, and the total absolute curvature function t-->|K| (t), total absolute curvature of the level {f=t}. We show they admits at most finitely many discontinuities.

2007-08-03abs ↗pdf ↗

Paper studies geometric properties of nonlinear Lebesgue spaces.

problem Geometric and analytic properties of nonlinear Lebesgue spaces.
method Formalizes pointwise description of geometric properties using a nonlinear Fubini-Lebesgue theorem.
result Definition of length structure, Alexandrov curvature bounds, and speed for absolutely continuous curves in nonlinear Lebesgue spaces.

In this paper we investigate the relationship between a general existence of transport maps of optimal couplings with absolutely continuous first marginal and the property of the background measure called essentially non-branching introduced by Rajala-Sturm (Calc.Var.PDE 2014). In particular, it is shown that the quali…

2017-04-18abs ↗pdf ↗

Neural networks approximate high-dimensional functions better than theory predicts.

problem Current theory struggles to explain why small neural networks work well in high-dimensional inverse problems.
method Bounding complexity required for neural networks to approximate Hölder or uniformly continuous functions on high-dimensional sets.
result A general theoretical framework explaining empirical successes of smaller networks in inverse problems.

Two markets should be considered isomorphic if they are financially indistinguishable. We define a notion of isomorphism for financial markets in both discrete and continuous time. We then seek to identify the distinct isomorphism classes, that is to classify markets. We classify complete one-period markets. We define …

2018-10-08abs ↗pdf ↗

LALR adapts learning rate for faster convergence in regression and neural nets.

problem Finding optimal learning rates for faster convergence in regression and neural networks.
method Lipschitz continuity theory applied to Mean Absolute Error and Quantile loss functions.
result Adaptive learning rate policy enables up to 20x faster convergence.

Establishes a link between risk measures and uniform integrability in finance.

problem Understanding uniform integrability in the context of financial risk measures.
method Introduces the folding score of distortion risk measures to study uniform integrability directly with gains and losses.
result Obtains three sets of equivalent conditions for uniform integrability involving coherent risk measures.

Enhances DIM to match learned representations to a specific distribution.

problem Learning representations conforming to a specific distribution.
method Injecting noise into normalized outputs of the encoder while keeping the InfoMax training objective.
result Learning uniformly and normally distributed representations, as well as representations of other absolutely continuous distributions.

Characterizes nodal volumes of Gaussian fields on manifolds, extending previous work.

problem Understanding the law and regularity of nodal volumes for Gaussian fields on manifolds.
method Gaussian measures, Morse theory, Malliavin-Sobolev spaces, ray absolute continuity.
result Extension and generalization of previous work on stationary fields to arbitrary dimensions.

This paper extends Euclidean theorems to anisotropic settings for varifolds.

problem Anisotropic mean curvature of codimension-one varifolds.
method Proves perpendicularity and locality of mean curvature for bounded anisotropic mean curvature varifolds.
result Anisotropic mean curvature agrees with the approximate mean curvature on the rectifiable part of the varifold.

It is well-known that the class of piecewise smooth curves together with a smooth Riemannian metric induces a metric space structure on a manifold. However, little is known about the minimal regularity needed to analyze curves and particularly to study length-minimizing curves where neither classical techniques such as…

2012-12-31abs ↗pdf ↗

Paper relaxes SGD privacy and generalization guarantees for non-smooth convex losses.

problem Privacy and generalization in SGD for non-smooth convex losses.
method Relaxes Lipschitz and strong smoothness assumptions to Hölder smoothness, proving (ε,δ)(ε,δ)-DP and optimal excess risk.
result Noisy SGD with αα-Hölder smooth losses achieves optimal excess risk with linear gradient complexity for α1/2α \geq 1/2.

This paper develops GPCA for probability distributions using Otto-Wasserstein geometry.

problem Analyzing modes of variation in datasets of probability measures.
method Geodesic Principal Component Analysis (GPCA) on Wasserstein space with neural networks.
result Identification of geodesic curves that capture modes of variation in probability distributions.

The paper explores MAE as a loss function for DNN vector-to-vector regression, proving its advantages over MSE.

problem Improving loss function for deep neural network based vector-to-vector regression.
method Presenting performance bounds and new properties of MAE, deriving generalized upper bounds, and interpreting MAE as a Laplacian distribution.
result MAE is a more suitable loss function than MSE for DNN based vector-to-vector regression, especially when errors follow a Laplacian distribution.

Paper improves classification rates for private data.

problem Classifying data with privacy constraints and relaxed assumptions.
method Introduced a novel approach for classification under privacy constraints, relaxing the strong density assumption.
result Achieved minimax optimal convergence rates without strong density assumption.