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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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131263394525 · Jun 202019922001200920172026
48 results for universal spaces

Metric spaces with certain curvature properties are universally infinitesimally Hilbertian.

problem Analyzing the infinitesimal geometry of metric spaces with curvature bounds.
method Proving a metric space with a Gromov-Hausdorff tangent splitting property is universally infinitesimally Hilbertian.
result Metric spaces with curvature bounds are universally infinitesimally Hilbertian.

WWe define the notion of a random metric space and prove that with probability one such a space is isometricto the Urysohn universal metric space. The main technique is the study of universal and random distance matrices; we relate the properties of metric (in particulary universal) space to the properties of distance …

2004-02-16abs ↗pdf ↗

We introduce a class of metrics on gauge theoretic moduli spaces. These metrics are made out of the universal matrix that appears in the universal connection construction of M. S. Narasimhan and S. Ramanan. As an example we construct metrics on the c_{2}=1 SU(2) moduli space of instantons on R^4 for various universal m…

2003-11-12abs ↗pdf ↗

We extend a recently proposed 1-nearest-neighbor based multiclass learning algorithm and prove that our modification is universally strongly Bayes-consistent in all metric spaces admitting any such learner, making it an "optimistically universal" Bayes-consistent learner. This is the first learning algorithm known to e…

2019-06-24abs ↗pdf ↗

We study the natural Kähler metrics on moduli spaces of stable oriented pairs in a very general framework, and we prove a universal formula expressing the Kähler class of such a moduli space in terms of characteristic classes of the universal bundle. We use these results to compute explicitly the volumina of certain Qu…

2013-12-21abs ↗pdf ↗

Develops a new approach to establish universality for any-dimensional machine learning models.

problem Understanding universality for models with inputs of varying sizes.
method Identifies any-dimensional functions with a unique function in an infinite-dimensional limit space, using symmetries and relations between inputs of different sizes.
result Establishes universality for several existing architectures and proposes modifications to restore it.

The paper finds universal inequalities for eigenvalues on hyperbolic spaces.

problem Eigenvalues of the Dirichlet Laplacian on conformally flat Riemannian manifolds.
method Establishes universal inequalities for eigenvalues of the Dirichlet Laplacian on hyperbolic spaces.
result Establishes universal inequalities for eigenvalues of the Dirichlet Laplacian on hyperbolic spaces.

Study shows kk-NN classifier is not universally consistent on (0,1)(0,1) but consistent on discrete and specific measure spaces.

problem Consistency of kk-NN classifier under Wasserstein distance on measure spaces.
method Analysis of kk-NN classifier properties under Wasserstein distance, use of σσ-finite metric dimension, geodesic structures of Wasserstein spaces.
result Consistency of kk-NN classifier on specific measure spaces (discrete, Gaussian, wavelet series) but not on (0,1)(0,1).

The study constructs a dense orbit in the universal commensurability augmented Teichmüller space.

problem Understanding the dense orbit in the universal commensurability augmented Teichmüller space.
method Using isometric embeddings and directed limits of augmented Teichmüller and moduli spaces.
result The action of the universal commensurability modular group on the universal commensurability augmented Teichmüller space produces a dense orbit.

The study characterizes quasiperiodic surfaces in pseudo-hyperbolic spaces with curvature conditions.

problem Characterizing quasiperiodic surfaces in pseudo-hyperbolic spaces.
method Curvature conditions, Gromov hyperbolicity, conformal hyperbolicity.
result Limit curves of quasiperiodic surfaces in the Einstein Universe have canonical quasisymmetric parametrizations.

Universal approximation for stochastic processes using Brownian motion.

problem Approximating stochastic processes with linear functionals.
method Establishing LpL^p-type universal approximation theorems for rough path spaces.
result Linear functionals on the signature of time-extended Brownian motion can approximate any pp-integrable stochastic process.

We construct a universal space for the class of proper metric spaces of bounded geometry and of given asymptotic dimension. As a consequence of this result, we establish coincidence of the asymptotic dimension with the asymptotic inductive dimensions.

2002-11-04abs ↗pdf ↗

For each nn, we construct a separable metric space Un\mathbb{U}_n that is universal in the coarse category of separable metric spaces with asymptotic dimension (asdim\mathop{asdim}) at most nn and universal in the uniform category of separable metric spaces with uniform dimension (udim\mathop{udim}) at most nn. Thus, $\m…

2017-08-11abs ↗pdf ↗

TVS-FNNs can approximate any continuous function on expanded input spaces.

problem Processing a broader range of inputs like sequences and matrices.
method Proving a universal approximation theorem for TVS-FNNs.
result TVS-FNNs can approximate any continuous function on expanded input spaces.

The paper explores simple and relatively simple transformation groups and their universal coverings.

problem Understanding the structure of universal coverings of transformation groups.
method Study of relatively simple groups and generalization of Tsuboi's metric space.
result Tsuboi's metric space of Ham~(M,ω)\widetilde{\mathrm{Ham}}(M, ω) is not quasi-isometric to the half line.

The paper shows neural networks can approximate functions over non-compact domains with non-polynomial activation.

problem Approximating functions over non-compact domains using neural networks.
method Using single-hidden-layer feedforward neural networks with non-polynomial activation functions over non-compact subsets of Euclidean spaces.
result Neural networks can approximate functions in weighted CkC^k-spaces and weighted Sobolev spaces over unbounded domains.

We define and give explicit construction of the universal tree-graded space with a given collection of pieces. We apply that to proving uniqueness of asymptotic cones of relatively hyperbolic groups whose peripheral subgroups have unique asymptotic cones. Modulo the Continuum Hypothesis, we show that if an asymptotic c…

2010-10-18abs ↗pdf ↗

The Einstein universe is the conformal compactification of Minkowski space. It also arises as the ideal boundary of anti-de Sitter space. The purpose of this article is to develop the synthetic geometry of the Einstein universe in terms of its homogeneous submanifolds and causal structure, with particular emphasis on d…

2007-06-20abs ↗pdf ↗

Study properties of balanced hyperbolic compact complex manifolds.

problem Understanding cohomology and harmonic spaces of balanced hyperbolic manifolds.
method Proved vanishing theorems and Hard Lefschetz-type theorems for balanced hyperbolic compact complex manifolds.
result Non-existence of certain L1L^1 currents on the universal covering space of a balanced hyperbolic manifold.

Study of laminations for pseudo-Anosov flows on three-manifolds.

problem Understanding laminations for pseudo-Anosov flows on three-manifolds.
method Analyzing laminations Λu±Λ^\pm_u for pseudo-Anosov orbit space universal circles, using prelaminations and results from Barthelmé, Bonatti, and Mann.
result Laminations Λu+Λ^+_u and ΛuΛ^-_u are completely determined by prelaminations on the boundary of the orbit space.

We study the geometry of universal embedding spaces for compact almost complex manifolds of a given dimension. These spaces are complex algebraic analogues of twistor spaces that were introduced by J-P. Demailly and H. Gaussier. Their original goal was the study of a conjecture made by F. Bogomolov, asserting the "tran…

2019-05-15abs ↗pdf ↗

Deep Sets approximates functions on sets with high-dimensional latent space.

problem Modeling functions of sets (permutation-invariant functions).
method Deep Sets, a method known to be a universal approximator for continuous set functions.
result Deep Sets' universal approximation property is only guaranteed with a sufficiently high-dimensional latent space.

New method for reducing dimensions of distributional data.

problem Nonlinear sufficient dimension reduction for distribution-on-distribution regression.
method Building universal kernels on metric spaces to characterize conditional independence.
result Method outperforms competing methods in synthetic and real data applications.