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.
This paper presents an efficient algorithm for evolving point cloud data on smooth manifolds using B-Splines.
This paper is devoted to the application of B-splines to volatility modeling, specifically the calibration of the leverage function in stochastic local volatility models and the parameterization of an arbitrage-free implied volatility surface calibrated to sparse option data. We use an extension of classical B-splines …
New method speeds up sparse Gaussian processes for large datasets.
Parametric spatial transformation models have been successfully applied to image registration tasks. In such models, the transformation of interest is parameterized by a fixed set of basis functions as for example B-splines. Each basis function is located on a fixed regular grid position among the image domain, because…
Dropout improves regularization in flexible models for rare features.
Proposes a new model for high-dimensional data analysis with unknown link function.
Enhances FM models for numerical features using function basis encoding.
A new model approximates complex functions in parameter space.
Sinh-acceleration speeds up B-spline option pricing.
Group convolutional neural networks (G-CNNs) can be used to improve classical CNNs by equipping them with the geometric structure of groups. Central in the success of G-CNNs is the lifting of feature maps to higher dimensional disentangled representations, in which data characteristics are effectively learned, geometri…
A new method evolves point clouds using B-splines for smooth surfaces.
Paper finds maximum curvature of Bézier-spline curves.
A machine learning method selects optimal orthonormal bases for functional data analysis.
Bayesian nonparametric LABS model adapts to function smoothness in Besov spaces.
We present an approach for polarimetric Synthetic Aperture Radar (SAR) image region boundary detection based on the use of B-Spline active contours and a new model for polarimetric SAR data: the GHP distribution. In order to detect the boundary of a region, initial B-Spline curves are specified, either automatically or…
Adaptive RBF-KAN improves KANs by dynamically adjusting kernel parameters.
Many attempts have been made in recent decades to integrate machine learning (ML) and topological data analysis. A prominent problem in applying persistent homology to ML tasks is finding a vector representation of a persistence diagram (PD), which is a summary diagram for representing topological features. From the pe…
BEKAN uses RBFs and evolutionary methods to solve PDEs with boundary conditions.
Kolmogorov-Arnold Networks achieve optimal convergence rates in nonparametric regression.
Efficient numerical method for time-fractional Black-Scholes model.
Locally-verifiable conditions ensure exactness of spline discrete de Rham complex.
We propose a sequential learning policy for noisy discrete global optimization and ranking and selection (R\&S) problems with high dimensional sparse belief functions, where there are hundreds or even thousands of features, but only a small portion of these features contain explanatory power. We aim to identify the spa…
Motivated by applications in architecture and design, we present a novel method for increasing the developability of a B-spline surface. We use the property that the Gauss image of a developable surface is 1-dimensional and can be locally well approximated by circles. This is cast into an algorithm for thinning the Gau…
ADVI speeds up Bayesian inference for bridge regression models.
Develops flexible non-parametric ACFs using B-spline kernels.
Deep-SITAR uses autoencoders to predict growth patterns.
Framework for designing nonlinearities in neural networks with slope constraints.
Efficiently estimates covariance for sparse functional data.
Approximating complex curves with simple parametric curves is widely used in CAGD, CG, and CNC. This paper presents an algorithm to compute a certified approximation to a given parametric space curve with cubic B-spline curves. By certified, we mean that the approximation can approximate the given curve to any given pr…
Study prenatal PM2.5 exposure and 4th grade reading scores, identifying critical windows of susceptibility.
DecompKAN improves time series forecasting accuracy and transparency.
Revisits stochastic collocation with exponential splines for option pricing.
KANHedge improves hedging of high-dimensional options using learnable B-spline activation functions.
RST improves environmental time series classification accuracy using randomized B-spline trees.
Kolmogorov-Arnold Networks promise scalable performance in high dimensions.
T-KAN improves HFT LOB forecasting with learnable splines.
PIE-PINN estimates elastic properties from noisy, low-res displacement data.
Independent component analysis (ICA) has been widely used for blind source separation in many fields such as brain imaging analysis, signal processing and telecommunication. Many statistical techniques based on M-estimates have been proposed for estimating the mixing matrix. Recently, several nonparametric methods have…
Isogeometric analysis is a recently developed computational approach that integrates finite element analysis directly into design described by non-uniform rational B-splines (NURBS). In this paper we show that price surfaces that occur in option pricing can be easily described by NURBS surfaces. For a class of stochast…
We develop a new nonparametric approach for estimating the risk-neutral density of asset prices and reformulate its estimation into a double-constrained optimization problem. We evaluate our approach using the S\&P 500 market option prices from 1996 to 2015. A comprehensive cross-validation study shows that our approac…
We propose a nonparametric method for detecting nonlinear causal relationship within a set of multidimensional discrete time series, by using sparse additive models (SpAMs). We show that, when the input to the SpAM is a -mixing time series, the model can be fitted by first approximating each unknown function with a …
Deep neural network is a state-of-art method in modern science and technology. Much statistical literature have been devoted to understanding its performance in nonparametric estimation, whereas the results are suboptimal due to a redundant logarithmic sacrifice. In this paper, we show that such log-factors are not nec…
Finsler space is differentiable manifold for which Minkowski space is the fiber of the tangent bundle. To understand structure of the reference frame in Finsler space, we need to understand the structure of orthonormal basis in Minkowski space. In this paper, we considered the definition of orthonormal basis in Minkows…
Stochastic volatility (SV) models mimic many of the stylized facts attributed to time series of asset returns, while maintaining conceptual simplicity. The commonly made assumption of conditionally normally distributed or Student-t-distributed returns, given the volatility, has however been questioned. In this manuscri…
New basis confirms Thurston's conjecture and reveals knot configurations.
Finsler space is differentiable manifold for which Minkowski space is the fiber of the tangent bundle. To understand structure of the reference frame in Finsler space, we need to understand the structure of orthonormal basis in Minkowski space. In this paper, I considered the definition of orthonormal basis in Minkowsk…
We study a novel spline-like basis, which we name the "falling factorial basis", bearing many similarities to the classic truncated power basis. The advantage of the falling factorial basis is that it enables rapid, linear-time computations in basis matrix multiplication and basis matrix inversion. The falling factoria…
Uber optimizes marketplace levers using machine learning to improve resource allocation efficiency.