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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.

169,181 papers · 148 categories

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48 results for piecewise smooth functions

Deep neural networks with piecewise-polynomial activations can approximate smooth functions and their derivatives.

problem Approximating smooth functions and their derivatives with neural networks.
method Derives the depth, width, and sparsity required for approximation in Hölder norms.
result Deep neural networks with bounded weights can approximate Hölder smooth functions and their derivatives.

The paper extends a variance gamma model to quadratic functions, reducing arbitrage and computational costs.

problem Creating an arbitrage-free interpolation for option pricing models.
method Generalizing the local variance gamma model to a piecewise quadratic local variance function.
result The quadratic model results in an arbitrage-free interpolation of class C3, reducing knots and computational cost.

Deep ReLU networks can approximate piecewise smooth functions efficiently.

problem Approximating piecewise smooth functions with neural networks.
method Constructing neural networks with fixed depth and weights to approximate functions from Eβ(Rd)\mathcal{E}^β(\mathbb R^d) up to L2L^2 error.
result Optimal approximation rate requires ReLU networks of a certain depth and number of weights.

We study online optimization of smoothed piecewise constant functions over the domain [0, 1). This is motivated by the problem of adaptively picking parameters of learning algorithms as in the recently introduced framework by Gupta and Roughgarden (2016). Majority of the machine learning literature has focused on Lipsc…

2016-04-07abs ↗pdf ↗

New GP model estimates piecewise continuous functions.

problem Piecewise continuous regression functions in scientific and engineering applications.
method Local Gaussian process model with partitioned local data and joint estimation of boundaries.
result Superior performance over conventional GP models in estimating piecewise regression functions.

Transformers struggle to approximate smooth functions, relying on piecewise constant approximations.

problem Understanding the expressivity of Transformers for function approximation.
method Theoretical analysis and experimental validation of Transformer's ability to approximate smooth functions.
result Transformers cannot reliably approximate smooth functions, relying on piecewise constant approximations.

The paper derives error bounds for piecewise smooth and switching regression models.

problem Regression problems with target functions switching between different modes.
method Derives generalization error bounds using Rademacher complexities and chaining arguments.
result Error bounds with radical dependency on the number of modes for piecewise smooth regression, and linear dependency for switching regression.

Piecewise linear activations create many spurious local minima in neural networks.

problem Understanding the loss surface of neural networks with piecewise linear activations.
method Proved the existence of infinite spurious local minima and partitioned the loss surface into smooth cells.
result Piecewise linear activations create many spurious local minima that are invariant under a continuous path.

Given a piecewise linear (PL) function pp defined on an open subset of Rn\R^n, one may construct by elementary means a unique polyhedron with multiplicities $\D(p)$ in the cotangent bundle Rn×Rn\R^n\times \R^{n*} representing the graph of the differential of pp. Restricting to dimension 2, we show that any smooth functi…

2013-05-09abs ↗pdf ↗

New algorithm reduces regret in online learning for piecewise continuous functions.

problem Exponential loss in efficiency when moving from classical to adversarial learning.
method Introduces generalized bracketing numbers and Follow-the-Perturbed-Leader algorithm.
result Optimal scaling of optimization oracle calls with average regret.

Smoothing graphons improve link prediction in Bayesian SBM without increasing computational complexity.

problem Accurate modeling of exchangeable relational data with flexible and computationally efficient graphons.
method Introducing smoothing procedures to piecewise-constant graphons to create smoothing graphons, which allow continuous intensity values for relations.
result Smoothing graphons improve AUC and precision for link prediction in real-world data sets.

We consider the problem of defining the structure of a smooth manifold on the various spaces of piecewise-smooth loops in a smooth finite dimensional manifold. We succeed for a particular type of piecewise-smooth loops. We also examine the action of the diffeomorphism group of the circle. It is not a useful action on t…

2008-03-05abs ↗pdf ↗

New TVD estimator adapts to piecewise constant functions, improving performance.

problem Improving TVD estimator performance for piecewise constant functions.
method Investigates adaptivity of TVD estimator to piecewise constant functions and proposes a data-driven tuning parameter.
result The ideally tuned TVD estimator performs better than in the worst case for piecewise constant functions.

Generalizes Thurston's jiggling lemma for piecewise smooth solutions.

problem Creating piecewise smooth solutions of differential relations without homotopical assumptions.
method Jiggling arbitrary sections of EE to construct solutions of R\mathcal{R}.
result Generalization of Thurston's lemma for piecewise smooth solutions of differential relations.

Extends Tanimoto kernel to real-valued functions.

problem Measuring similarity between real-valued functions.
method Unified representation of real-valued functions via sets, derived general form of the kernel, explicit feature representation, and smooth approximation.
result General Tanimoto kernel for real-valued functions.

We prove that every piecewise linear manifold of dimension up to four on which a finite group acts by piecewise linear homeomorphisms admits a compatible smooth structure with respect to which the group acts smoothly. This solves a challenge posed by Thurston in dimension three and confirms a conjecture by Kwasik and L…

2015-07-09abs ↗pdf ↗

The knot invariant Upsilon, defined by Ozsvath, Stipsicz, and Szabo, induces a homomorphism from the smooth knot concordance group to the group of piecewise linear functions on the interval [0,2]. Here we define a set of related secondary invariants, each of which assigns to a knot a piecewise linear function on [0,2].…

2016-10-17abs ↗pdf ↗

Discrete forms of the scalar, sectional and Ricci curvatures are constructed on simplicial piecewise flat triangulations of smooth manifolds, depending directly on the simplicial structure and a choice of dual tessellation. This is done by integrating over volumes which include appropriate samplings of hinges for each …

2016-03-10abs ↗pdf ↗

Rham cohomology of Lie groupoids over triangulated manifolds is isomorphic to piecewise cohomology.

problem Establishing Rham cohomology for Lie groupoids over triangulated manifolds.
method Using isomorphism between Lie algebroid cohomology and piecewise smooth cohomology, proving Rham cohomology is isomorphic to piecewise Rham cohomology.
result Piecewise de Rham cohomology of a Lie groupoid does not depend on the triangulation of the base.

Estimates piecewise polynomials and bounded variation functions using optimal decision trees.

problem Estimating piecewise smooth functions in general dimensions.
method Dyadic CART and Optimal Regression Tree (ORT) estimators for piecewise polynomials and bounded variation functions.
result Oracle inequalities and risk bounds for ORT estimators, demonstrating adaptivity and optimality.

Piecewise flat approximations for curvature in Euclidean and non-Euclidean spaces.

problem Approximating local extrinsic curvature on discrete manifolds.
method Constructing discrete curvature forms on piecewise flat manifolds, using weighted sums of hinge angles.
result Converges to smooth curvature values as mesh refinement occurs, favorably comparing with other discrete approaches.

New method samples from piecewise smooth distributions using Hamiltonian Monte Carlo.

problem Sampling from distributions with discontinuous gradients.
method Generalized Randomized Hamiltonian Monte Carlo (GRHMC) for piecewise smooth targets.
result GRHMC processes sample from piecewise smooth target distributions with the desired distribution as the invariant distribution.

Generalizes Poincaré-Hopf Theorem for piecewise smooth boundaries.

problem Conservation law for vector fields on surfaces with piecewise smooth boundaries.
method Generalization of the Poincaré-Hopf Theorem for real-analytic vector fields on surfaces with piecewise smooth boundaries.
result Conservation law for vector fields on surfaces with piecewise smooth boundaries.

The paper develops methods to approximate quantities of interest in insurance models using deterministic integration.

problem Computing quantities of interest in insurance models, such as the probability of ruin and insurance company value.
method Adapting the problem to allow for deterministic numerical integration algorithms, including quasi-Monte Carlo rules and smoothing techniques.
result Convergence result justifying phase-type approximations on the process level.

The paper approximates smooth isotropic surfaces with piecewise linear ones.

problem Approximating smooth isotropic surfaces with piecewise linear ones.
method Using analogies with infinite dimensional moment map geometry, the authors prove the approximation of smooth isotropic immersions by piecewise linear ones.
result Smooth isotropic immersions can be approximated by piecewise linear isotropic maps.

The paper introduces a scalable unsupervised learning framework to improve deep neural networks.

problem Improving deep neural networks' performance and generalization in unsupervised settings.
method A scalable unsupervised regularization framework that constrains hypothesis space to non-trivial piecewise constant functions.
result The framework leads to a factually confident and smooth discriminative model, achieving state-of-the-art clustering results and generalization on both synthetic and real data.

Smooths C0C^0-Finsler structures using mollifiers.

problem Constructing smooth approximations of discontinuous Finsler structures.
method Convolution of C0C^0-Finsler structure with standard mollifier to create C\mathit{C}^\infty-Finsler structures.
result Uniform convergence of Finsler structures and their geometric properties to the original structure.

Uniqueness of stable, non-smooth hypersurfaces with constant anisotropic mean curvature.

problem Identifying stable, non-smooth hypersurfaces with constant anisotropic mean curvature.
method Study of piecewise-smooth hypersurfaces with anisotropic energy, proving uniqueness of the Wulff shape under certain conditions.
result Closed stable equilibrium hypersurfaces are unique and the Wulff shape when the anisotropic energy density is twice continuously differentiable and convex.

Study curvature of piecewise metrics using moving frames.

problem Deriving a curvature measure for piecewise-smooth Riemannian metrics.
method Used moving frame techniques to derive curvature, showing it satisfies Cartan structure equations and gauge transformation law.
result Equivalence of the derived curvature to existing densitized distributional curvature.

New SGD covering technique yields dimension-independent generalization bounds.

problem Generalization of stochastic gradient descent in non-convex, non-smooth settings.
method Localized ε-covers for SGD trajectories, showing dimension-independent complexity.
result Generalization error upper bounded by O((lognlog(nP))/n)O(\sqrt{(\log n\log(nP))/n}).

We recast basic topological concepts underlying differential geometry using the language and tools of noncommutative geometry. This way we characterize principal (free and proper) actions by a density condition in (multiplier) C*-algebras. We introduce the concept of piecewise triviality to adapt the standard notion of…

2006-12-31abs ↗pdf ↗

Discretizations of the mean curvature and extrinsic curvature components are constructed on piecewise flat simplicial manifolds, giving approximations for smooth curvature values in a mostly mesh-independent way. These constructions are given in combinatoric form in terms of the extrinsic hinge angles, the intrinsic st…

2016-12-22abs ↗pdf ↗

The study examines metrics on Riemannian spaces with bounded properties and finds conditions for Lipschitz and uniform bounds.

problem Investigating bounded rough Riemannian metrics and their properties.
method Analyzing the structure of bounded rough Riemannian metrics and finding conditions for Lipschitz and uniform bounds.
result Weak conditions are identified for Lipschitz and uniform bounds on the metrics.

A new shape space allows optimization of non-smooth shapes in fluid mechanics.

problem Optimizing non-smooth shapes in fluid mechanics.
method Constructing a product manifold to include piecewise-smooth shapes.
result Numerical results show applicability in minimizing viscous energy dissipation.