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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.

168,742 papers · 148 categories

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25.0%50.0%75.0%100.0% · Sep 199219922001200920172026
48 results for closed paths

Introduces q-paths for generalizing geometric annealing paths in machine learning.

problem Limited applicability of existing path methods in machine learning.
method Develops a family of paths derived from a generalized mean, including geometric and arithmetic mixtures.
result Empirical gains in Bayesian inference and generative model evaluation.

One-shot path planning for multiple agents using neural networks.

problem Efficiently generating optimal or near-optimal paths for multiple agents in robotics.
method Utilizes fully convolutional neural networks for one-shot multi-agent path planning.
result Demonstrates successful generation of optimal or near-optimal paths in over 85% of cases for multi-path planning.

We consider a continuous path of bounded symmetric Fredholm bilinear forms with arbitrary endpoints on a real Hilbert space, and we prove a formula that gives the spectral flow of the path in terms of the spectral flow of the restriction to a finite codimensional closed subspace. We also discuss the case of restriction…

2008-01-26abs ↗pdf ↗

In this note it is shown that the Maslov Index for pairs of Lagrangian Paths as introduced by Cappell, Lee and Miller appears by parallel transporting elements of (a certain complex line-subbundle of) the symplectic spinorbundle over Euclidean space, when pulled back to an (embedded) Lagrangian submanifold LL, along c…

2008-11-17abs ↗pdf ↗

Derives path integrals for perturbative strings on various backgrounds.

problem Calculating path integrals for strings on curved backgrounds.
method Derives path integrals from string geometry theory by considering fluctuations around string backgrounds.
result Derives path integrals of all order perturbative strings on various backgrounds.

Wilson lines in gauge theories admit several path integral descriptions. The first one (due to Alekseev-Faddeev-Shatashvili) uses path integrals over coadjoint orbits. The second one (due to Diakonov-Petrov) replaces a 1-dimensional path integral with a 2-dimensional topological σσ-model. We show that this σσ-model i…

2015-07-22abs ↗pdf ↗

Closed-form formulas for path-independent options in a specific Lévy model.

problem Valuation of path-independent options in the exponential NIG model.
method Closed-form pricing formulas derived using a factorized representation in Mellin space and complex analysis.
result Valid closed-form formulas with quickly convergent series for various options.

We derive a closed-form solution for the price of an average price as well as an average strike geometric Asian option, by making use of the path integral formulation. Our results are compared to a numerical Monte Carlo simulation. We also develop a pricing formula for an Asian option with a barrier on a control proces…

2009-06-24abs ↗pdf ↗

We solve the paradox of score-based methods by minimizing path variance.

problem Score-based methods are path-dependent, leading to inaccurate and unstable estimators.
method Propose MVP Principle to minimize path variance, derive closed-form expression, and use flexible Kumaraswamy Mixture Model.
result Establishes new state-of-the-art results on challenging benchmarks.

A hyperlink is a finite set of non-intersecting simple closed curves in R×R3\mathbb{R} \times \mathbb{R}^3. We compute the Wilson Loop observable using a path integral with an Einstein-Hilbert action. Using axial-gauge fixing, we can write this path integral as the limit of a sequence of Chern-Simons integrals, studied e…

2017-01-11abs ↗pdf ↗

A positive path in the linear symplectic group $\Sp(2n)$ is a smooth path which is everywhere tangent to the positive cone. These paths are generated by negative definite (time-dependent) quadratic Hamiltonian functions on Euclidean space. A special case are autonomous positive paths, which are generated by time-indepe…

1996-06-18abs ↗pdf ↗

This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs-Boltzmann distribution is defined over a bag of paths in a network, that is, on a representati…

2013-02-27abs ↗pdf ↗

Obstructions found for closed Fedosov star products on symplectic and Kähler manifolds.

problem Existence of closed Fedosov star products on symplectic and Kähler manifolds.
method Normalized trace of Fedosov star product, cohomology classes, and formal 2-forms.
result Integral invariants attached to symplectic and Kähler manifolds as obstructions to closed Fedosov star products.

The author has previously constructed a class of admissible vector fields on the path space of an elliptic diffusion process xx taking values in a closed compact manifold. In this Note the existence of flows for this class of vector fields is established and it is shown that the law of xx is quasi-invariant under the…

2006-06-15abs ↗pdf ↗

A new formula connects supersymmetric path integrals to Chern-Simons theory.

problem Constructing a rigorous path integral for supersymmetric theories on spin manifolds.
method Using Chen differential forms and non-commutative geometry, a Chern-Simons transgression formula is derived.
result The supersymmetric path integral induces a differential topological invariant.

Develops a new solver for path-dependent PDEs using signature kernels.

problem Solving path-dependent PDEs (PPDEs) efficiently and accurately.
method Uses signature kernels to solve PPDEs by approximating the solution with minimal norm in a reproducing kernel Hilbert space.
result Proves the consistency of the numerical scheme, ensuring convergence to PPDE solutions as the number of collocation points increases.

The paper provides an efficient method to price path-dependent derivatives using multiscale stochastic volatility models.

problem Pricing path-dependent derivatives under multiscale stochastic volatility models.
method Derives a Malliavin representation for the first-order approximation of the price of path-dependent derivatives.
result An efficient Monte Carlo approximation for pricing path-dependent derivatives is derived.

Classifies braids with positive Artin presentations and their fundamental groups.

problem Classifying braids with positive Artin presentations and understanding their fundamental groups.
method Analyzing framed, closed pure n-braids in the 3-sphere to determine if they represent positive Artin presentations.
result Closed, pure n-braids B' in the 3-sphere that represent positive Artin presentations are strongly invertible, and some 3-manifolds do not admit such presentations.

The snake charmer algorithm permits us to deform a piecewise smooth curve starting from the origin in R^d, so that its end follows a given path. When this path is a loop, a holonomy phenomenon occurs. We prove that the holonomy orbits are closed manifolds diffeomorphic to real Stiefel manifolds. A survey of the snake c…

2006-03-27abs ↗pdf ↗

The paper calculates option prices using Mellin transform for stochastic volatility models.

problem Calculating prices for path-dependent options under stochastic volatility.
method Asymptotic approach and Mellin transform for deriving closed-form formulas.
result Derives closed-form formulas for option prices with first-order approximation.

Diagonal linear networks converge to lasso regularization path during training.

problem Understanding the regularization behavior of diagonal linear networks.
method Analyzing the training trajectory of diagonal linear networks and comparing it to the lasso regularization path.
result The training trajectory of diagonal linear networks is closely related to the lasso regularization path.

New method for efficient proximal mapping of 1-path-norm in shallow networks.

problem Efficiently handling the 1-path-norm of shallow neural networks.
method Closed-form proximal operator for efficient computation and upper bound on Lipschitz constant.
result Proximal mapping allows robust training against adversarial perturbations.