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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.

168,657 papers · 148 categories

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133265398530 · Jun 202019922001200920172026
48 results for nonuniform sampling

New approach to nonuniform learnability using measure theory.

problem Nonuniform learnability of hypotheses with varying sample sizes.
method Measure theoretic approach to redefine nonuniform learnability, introducing a new algorithm (Generalize Measure Learnability).
result Achieved statistical consistency in learning countable hypothesis classes.

New method shows Hessian estimator from random samples converges to true Hessian on complex manifolds.

problem Uncertainty in Hessian estimator accuracy on complex manifolds with boundaries and nonuniform sampling.
method Locally fitting quadratic polynomials, rigorous theoretical analysis under mild conditions.
result The Hessian estimator asymptotically converges to the true Hessian, even near boundaries.

This paper studies the covolumes of nonuniform arithmetic lattices in PU(n, 1). We determine the smallest covolume nonuniform arithmetic lattices for each n, the number of minimal covolume lattices for each n, and study the growth of the minimal covolume as n varies. In particular, there is a unique lattice (up to conj…

2011-07-26abs ↗pdf ↗

Improves statistical inference using machine learning predictions with imputed data.

problem Invalid statistical inference due to machine learning prediction errors.
method Bootstrap confidence intervals for nonuniform samples and arbitrary imputed features.
result Valid confidence intervals without assumptions on machine learning model quality.

Nonuniform tubular neighborhoods of curves in Euclidean n-space are studied by using weighted distance functions and generalizing the normal exponential map. Different notions of injectivity radii are introduced to investigate singular but injective exponential maps. A generalization of the thickness formula is obtaine…

2007-05-16abs ↗pdf ↗

Improved estimation for imbalanced data using log odds correction and optimal sampling.

problem Parameter estimation with nonuniform negative sampling for imbalanced data.
method Derive asymptotic distribution of IPW estimator, derive optimal sampling probability, propose likelihood-based estimator.
result Improved estimator has the smallest asymptotic variance.

For any n>1 we determine the uniform and nonuniform lattices of the smallest covolume in the Lie group Sp(n,1). We explicitly describe them in terms of the ring of Hurwitz integers in the nonuniform case with n even, respectively, of the icosian ring in the uniform case for all n>1.

2018-02-21abs ↗pdf ↗

Let ΓΓ be a nonuniform lattice acting on real hyperbolic n-space. We show that in dimension greater than or equal to 4, the volume of a representation is constant on each connected component of the representation variety of ΓΓ in SO(n,1). Furthermore, in dimensions 2 and 3, there is a semialgebraic subset of the repr…

2013-10-04abs ↗pdf ↗

If Gamma is a nonuniform, irreducible lattice in a semisimple Lie group whose real rank is greater than 1, we show Gamma contains a subgroup that is isomorphic to a nonuniform, irreducible lattice in either SL(3,R), SL(3,C), or a direct product SL(2,R)^m x SL(2,C)^n$, with m + n > 1. (In geometric terms, this can be in…

2007-05-30abs ↗pdf ↗

The paper explores the dynamics of composite symplectic Dehn twists with nonuniform hyperbolicity.

problem Understanding the dynamics and properties of composite symplectic Dehn twists.
method Analyzing the form of nonuniform hyperbolicity, growth of Floer cohomology, and classification of symplectic mapping classes.
result Composite symplectic Dehn twists exhibit positive topological entropy and exponential growth in Floer cohomology.

Deep networks adapt to function regularity and data distribution.

problem Understanding deep learning's adaptability to function regularity and data distribution.
method Developed nonparametric approximation and estimation theories for a broad class of functions using deep ReLU networks.
result Deep neural networks are adaptive to different regularity of functions and nonuniform data distributions.

Optimal multiscale learning of linear operators

problem Statistical and computational limits of learning bounded linear operators between Sobolev spaces
method Reformulate as an infinite-dimensional matrix regression problem with heterogeneous multiscale structure
result Establish minimax rates and construct a finite-resolution blockwise least-squares estimator attaining these rates

We prove noncoherence of certain families of lattices in the isometry group of the hyperbolic n-space for n greater than 3. For instance, every nonuniform arithmetic lattice in SO(n,1) is noncoherent, provided that n is at least 6.

2006-08-16abs ↗pdf ↗

In this paper we answer the following question: what is the infinitesimal generator of the diffusion process defined by a kernel that is normalized such that it is bi-stochastic with respect to a specified measure? More precisely, under the assumption that data is sampled from a Riemannian manifold we determine how the…

2017-11-17abs ↗pdf ↗

Perfect clustering achieved in hypergraphs with enough interactions.

problem Complexity and lack of tractable models for analyzing hypergraphs.
method Introduced an interaction hypergraph model for analyzing hypergraphs, defined latent embeddings, and analyzed spectral estimators.
result A spectral estimate of interaction latent positions can achieve perfect clustering with enough interactions.

New summary measures reveal geometric structure in weighted measures on manifolds.

problem Lack of geometric information in standard weight-only summaries.
method Heat-kernel entropy profiles, tracking nonuniformity across scales.
result Geometric effective sample size discounts nearby or duplicate particles.

We determine the number of cusps of minimal Picard modular surfaces. The proof also counts cusps of other Picard modular surfaces of arithmetic interest. Consequently, for each N > 0 there are finitely many commensurability classes of nonuniform arithmetic lattices in SU(2, 1) that contain an N-cusped surface. We also …

2010-05-12abs ↗pdf ↗

The existing approaches to intrinsic dimension estimation usually are not reliable when the data are nonlinearly embedded in the high dimensional space. In this work, we show that the explicit accounting to geometric properties of unknown support leads to the polynomial correction to the standard maximum likelihood est…

2019-04-12abs ↗pdf ↗

ERDMD discovers sparse, nonuniformly timed DMD models from chaotic attractors.

problem Discovering high-fidelity, nonuniformly timed DMD models from chaotic data.
method Entropic regression for nonlinear information flow detection, combined with multi-step DMD.
result ERDMD produces highly efficient and robust models with minimal complexity.

We prove Zimmer's conjecture for C2C^2 actions by finite-index subgroups of SL(m,Z)\mathrm{SL}(m,\mathbb{Z}) provided m>3m>3. The method utilizes many ingredients from our earlier proof of the conjecture for actions by cocompact lattices in SL(m,R)\mathrm{SL}(m,\mathbb{R}) but new ideas are needed to overcome the lack of compactn…

2017-10-07abs ↗pdf ↗

Framework for designing nonlinearities in neural networks with slope constraints.

problem Designing nonlinearities with specific properties for signal processing.
method Variational framework with regularization for slope constraints and optimization of adaptive splines.
result Adaptive nonuniform linear splines achieve global optimum in constrained optimization.

T-Rex uses EM to fit robust factor models in noisy data.

problem Robustly fitting factor models in high-dimensional data with heavy tails and outliers.
method Expectation-Maximization (EM) algorithm based on Tyler's M-estimator for elliptical distributions.
result Demonstrates robustness in direction-of-arrival estimation and subspace recovery.

This paper optimizes subsampling for large datasets using Poisson distribution.

problem Efficiently subsample large datasets for quasi-likelihood estimation.
method Derives optimal Poisson subsampling probabilities and develops a distributed subsampling framework.
result Consistent and asymptotically normal estimators are obtained.

If ΓΓ is any nonuniform lattice in the group PU(2,1){\rm PU}(2,1), let Γ\overlineΓ be the quotient of ΓΓ obtained by filling the cusps of ΓΓ (i.e. killing the center of parabolic subgroups). Assuming that such a lattice ΓΓ has positive first Betti number, we prove that for any sufficiently deep subgroup of finite index…

2014-10-30abs ↗pdf ↗

This paper proposes BAT to balance accuracy and robustness in adversarial training.

problem Balancing accuracy and robustness in adversarial training models.
method Blind adversarial training (BAT) uses a cutoff-scale strategy to adaptively estimate a nonuniform budget for AEs.
result BAT improves the overall robustness of adversarial training models.

Estimates functions on unknown manifolds using multiscale regression.

problem Regression on unknown low-dimensional manifolds embedded in high-dimensional spaces.
method Low-dimensional coordinates at multiple scales, local polynomial fitting, data-driven wavelet thresholding.
result Optimal learning rates for estimating functions with nonuniform regularity.

In this paper we investigate the effectiveness of Alternating Direction Implicit (ADI) time discretization schemes in the numerical solution of the three-dimensional Heston-Hull-White partial differential equation, which is semidiscretized by applying finite difference schemes on nonuniform spatial grids. We consider t…

2011-11-17abs ↗pdf ↗

Optimal subset selection for hypothesis testing with penalties.

problem Optimal subset selection of information sources for hypothesis testing with misclassification penalties.
method Proposes a misclassification penalty framework and studies two variants of subset selection problems under centralized Bayesian learning.
result Proves the submodularity of the objective and constraints of the subset selection problems and establishes performance guarantees for greedy algorithms.

We propose to optimize the activation functions of a deep neural network by adding a corresponding functional regularization to the cost function. We justify the use of a second-order total-variation criterion. This allows us to derive a general representer theorem for deep neural networks that makes a direct connectio…

2018-02-26abs ↗pdf ↗