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.
CoCP optimizes prediction intervals by jointly learning center and radius, improving efficiency and coverage.
BC-ACI corrects time series forecast bias, improving prediction intervals.
New quantile methods improve uncertainty quantification across various models.
Efficiently estimates Cox model coefficients without sharing data.
Developing an explainable outlier detection method for interval-valued data using Shapley value-based approach.
Skew-adaptive method improves prediction intervals for regression.
Paper discusses directional differentiability of interval-valued functions on Riemannian manifolds.
New method detects anomalies in computing centers' logs.
We study a parsimonious but non-trivial model of the latent limit order book where orders get placed with a fixed displacement from a center price process, i.e.\ some process in-between best bid and best ask, and get executed whenever this center price reaches their level. This mechanism corresponds to the fundamental …
Our model predicts stock market intervals using chaotic fusion and graph convolutional networks.
The paper tackles high-dimensional mixed linear regression with unknown parameters and proposes methods for estimation, confidence intervals, and hypothesis testing.
Hurricanes are cyclones circulating about a defined center whose closed wind speeds exceed 75 mph originating over tropical and subtropical waters. At landfall, hurricanes can result in severe disasters. The accuracy of predicting their trajectory paths is critical to reduce economic loss and save human lives. Given th…
The distribution of the return intervals between volatilities above a threshold for financial records has been approximated by a scaling behavior. To explore how accurate is the scaling and therefore understand the underlined non-linear mechanism, we investigate intraday datasets of 500 stocks which consist of …
A new method for streaming PCA provides confidence intervals for eigenvector entries.
Many scientific and engineering challenges -- ranging from personalized medicine to customized marketing recommendations -- require an understanding of treatment effect heterogeneity. In this paper, we develop a non-parametric causal forest for estimating heterogeneous treatment effects that extends Breiman's widely us…
GP-CATE calibrates CATE intervals in few-placebo trials with Gaussian processes.
A new Mapper algorithm optimizes data visualization through automatic parameter tuning.
Distributed statistical learning problems arise commonly when dealing with large datasets. In this setup, datasets are partitioned over machines, which compute locally, and communicate short messages. Communication is often the bottleneck. In this paper, we study one-step and iterative weighted parameter averaging in s…
New method constructs confidence sets for GLMs via game theory.
The first eigenvalue of the Laplacian on a unique Hurwitz surface has a sevenfold multiplicity and specific numerical values.
JAWS audits predictive uncertainty under covariate shift using jackknife+ weighted methods.
Center identified in stated skein algebra for quantum traces.
The object of our investigation is a point that gives the maximum value of a potential with a strictly decreasing radially symmetric kernel. It defines a center of a body in Rm. When we choose the Riesz kernel or the Poisson kernel as the kernel, such centers are called a radial center or an illuminating center, respec…
Gaussian process (GP) regression is a powerful interpolation technique due to its flexibility in capturing non-linearity. In this paper, we provide a general framework for understanding the frequentist coverage of point-wise and simultaneous Bayesian credible sets in GP regression. As an intermediate result, we develop…
Refines geometric center of mass analysis for Einstein field equations.
Let f:M->M be a partially hyperbolic diffeomorphism such that all of its center leaves are compact. We prove that Sullivan's example of a circle foliation that has arbitrary long leaves cannot be the center foliation of f. This is proved by thorough study of the accessible boundaries of the center-stable and the center…
We investigate centers of a body (the closure of a bounded open set) defined as maximum points of potentials. In particular, we study centers defined by the Riesz potential and by Poisson's integral. These centers, in general, depend on parameters and move with respect to the parameters. We give a necessary and suffici…
Outlier based Robust Principal Component Analysis (RPCA) requires centering of the non-outliers. We show a "bias trick" that automatically centers these non-outliers. Using this bias trick we obtain the first RPCA algorithm that is optimal with respect to centering.
We prove the existence of a center, or continuous selection of a point, in the relative interior of embedded -disks in Riemannian -manifolds. If the center can be made equivariant with respect to the isometries of the manifold, and under mild assumptions the same holds for . By contrast, for…
This work analyzes centered binary Restricted Boltzmann Machines (RBMs) and binary Deep Boltzmann Machines (DBMs), where centering is done by subtracting offset values from visible and hidden variables. We show analytically that (i) centering results in a different but equivalent parameterization for artificial neural …
The paper quantizes concatenated noisy vectors to a common cluster center, improving performance over naive methods.
In this study, we investigate the locus of the centers of the Meusnier spheres. Just as focal curve is the locus of the centers of the osculating spheres, we investigate the geometrical interpretation on the locus of the centers of the Meusnier spheres. We proved that if the curve is a principal line, the locus of the …
This article briefly introduced Arthur and Vassilvitshii's work on \textbf{k-means++} algorithm and further generalized the center initialization process. It is found that choosing the most distant sample point from the nearest center as new center can mostly have the same effect as the center initialization process in…
The determination of cluster centers generally depends on the scale that we use to analyze the data to be clustered. Inappropriate scale usually leads to unreasonable cluster centers and thus unreasonable results. In this study, we first consider the similarity of elements in the data as the connectivity of nodes in an…
Absolutely partially hyperbolic surface endomorphisms have a coherent center foliation.
Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused b…
Introduces Quantum Data Center for quantum era benefits.
Unified theory linking atom-centered and message-passing models for molecular properties.
Characterizes G2-structures on Lie algebras with non-trivial center.
Paper introduces new center of mass for flat manifolds.
Simplified proof of mass center system's uniqueness and generalized Pappus' theorem across Euclidean, spherical, and hyperbolic geometries.
New features for quantum calculations learn N-center Hamiltonian matrix elements.
Proposes two-stage robust and sparse distributed inference for large-scale data.
For nearly spherical bodies, the unique center is proven under certain conditions.
We show that the center of the Goldman algebra associated to a closed oriented hyperbolic surface is trivial. For a hyperbolic surface of finite type with nonempty boundary, the center consists of closed curves which are homotopic to boundary components or punctures.
The paper studies properties of stated SL(n)-skein algebras and their centers.
The center of a quotient group of piecewise linear homeomorphisms is trivial.
The classical notion of center of mass for an isolated system in general relativity is derived from the Hamiltonian formulation and represented by a flux integral at infinity. In contrast to mass and linear momentum which are well-defined for asymptotically flat manifolds, center of mass and angular momentum seem less …