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.
Sinh-acceleration speeds up B-spline option pricing.
A new method evolves point clouds using B-splines for smooth surfaces.
Paper finds maximum curvature of Bézier-spline curves.
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 …
This paper presents an efficient algorithm for evolving point cloud data on smooth manifolds using B-Splines.
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…
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…
Kolmogorov-Arnold Networks achieve optimal convergence rates in nonparametric regression.
Efficient numerical method for time-fractional Black-Scholes model.
Dropout improves regularization in flexible models for rare features.
New method speeds up sparse Gaussian processes for large datasets.
Locally-verifiable conditions ensure exactness of spline discrete de Rham complex.
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…
Develops flexible non-parametric ACFs using B-spline kernels.
A new model approximates complex functions in parameter space.
Deep-SITAR uses autoencoders to predict growth patterns.
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…
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…
Efficiently estimates covariance for sparse functional data.
Proposes a new model for high-dimensional data analysis with unknown link function.
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…
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.
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…
Enhances FM models for numerical features using function basis encoding.
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…
Uber optimizes marketplace levers using machine learning to improve resource allocation efficiency.
In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive conditional heteroscedastic (power-TGARCH) model. The mo…
TPBS models improve robustness to overfitting with localized Dirichlet energy regularization.
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…
For a wide range of clinical applications, such as adaptive treatment planning or intraoperative image update, feature-based deformable registration (FDR) approaches are widely employed because of their simplicity and low computational complexity. FDR algorithms estimate a dense displacement field by interpolating a sp…
The paper develops a neural network method for estimating drift functions of diffusion processes from discrete observations.
ADVI speeds up Bayesian inference for bridge regression models.
KAN-PCA improves asset return analysis by capturing more variance than classical PCA during market crises.
KAPLAN-HR models survival data without manual interactions, outperforming existing methods.
Framework for designing nonlinearities in neural networks with slope constraints.
Kolmogorov-Arnold Networks enable ultrafast online learning with fixed-point quantization.
We present SplineNets, a practical and novel approach for using conditioning in convolutional neural networks (CNNs). SplineNets are continuous generalizations of neural decision graphs, and they can dramatically reduce runtime complexity and computation costs of CNNs, while maintaining or even increasing accuracy. Fun…
New method weaves paper strips for designing curved surfaces with elasticity.