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.
Proposes a method to measure similarity between anomaly scores from different methods.
New estimator reveals intraday betas mainly driven by correlations.
We define scenarios, propose different methods of aggregating them, discuss their properties and benchmark them against quadrant requirements.
Gradient boosting decision tree (GBDT) is a widely-used machine learning algorithm in both data analytic competitions and real-world industrial applications. Further, driven by the rapid increase in data volume, efforts have been made to train GBDT in a distributed setting to support large-scale workloads. However, we …
Consider two insurance companies (or two branches of the same company) that receive premiums at different rates and then split the amount they pay in fixed proportions for each claim (for simplicity we assume that they are equal). We model the occurrence of claims according to a Poisson process. The ruin is achieved wh…
We introduce a new framework for training deep generative models for high-dimensional conditional density estimation. The Bottleneck Conditional Density Estimator (BCDE) is a variant of the conditional variational autoencoder (CVAE) that employs layer(s) of stochastic variables as the bottleneck between the input a…
Copula models have become popular in different applications, including modeling shocks, in view of their ability to describe better the dependence concepts in stochastic systems. The class of maxmin copulas was recently introduced by Omladič and Ružić. It extends the well known classes of Marshall-Olkin and Marshall co…
Differential forms and symmetric tensors show contrasting singular behaviors in a specific geometric setting.
The quotient of random variables with normal distributions is examined and proven to have have power law decay, with density , with the coefficient depending on the means and variances of the numerator and denominator and their correlation. We also obtain the conditional probability…
In this article we consider the Merton problem in a market with a single risky asset and transaction costs. We give a complete solution of the problem up to the solution of a free-boundary problem for a first-order differential equation, and find that the form of the solution (whether the problem is well-posed, whether…
We factorize the Dirac operator on the Connes-Landi 4-sphere in unbounded KK-theory. We show that a family of Dirac operators along the orbits of the torus action defines an unbounded Kasparov module, while the Dirac operator on the principal orbit space -an open quadrant in the 2-sphere- defines a half-closed chain. W…
We exhibit many examples of closed symplectic manifolds on which there is an autonomous Hamiltonian whose associated flow has no nonconstant periodic orbits (the only previous explicit example in the literature was the torus T^2n (n\geq 2) with an irrational symplectic structure). The underlying smooth manifolds of our…
In this paper we introduce some new copulas emerging from shock models. It was shown earlier that reflected maxmin copulas (RMM for short) are not just some specific singular copulas; they contain many important absolutely continuous copulas including the negative quadrant dependent part of the Eyraud-Farlie-Gumbel-Mor…
New insights into tail behavior of heavy-tailed random vectors and processes.
A new optimization method for probability simplex problems.
GCNs help in diagnosing label scarcity and feature quality on graphs.
The Morris Water Maze is commonly used in behavioural neuroscience for the study of spatial learning with rodents. Over the years, various methods of analysing rodent data collected in this task have been proposed. These methods span from classical performance measurements (e.g. escape latency, rodent speed, quadrant p…
Study examines financial market structure changes during the COVID-19 crash using a novel MI approach.
In this paper, we propose the use of a black-box optimization method called deterministic Mesh Adaptive Direct Search (MADS) algorithm with orthogonal directions (Ortho-MADS) for the selection of hyperparameters of Support Vector Machines with a Gaussian kernel. Different from most of the methods in the literature that…
Upper bounds for CV errors apply to lasso and other models.
Let M be a complete n-dimensional Riemannian spin manifold, partitioned by q two-sided hypersurfaces which have a compact transverse intersection N and which in addition satisfy a certain coarse transversality condition. Let E be a Hermitean bundle with connection on M. We define a coarse multi-partitioned index of the…
Study sets a nontrivial upper limit on return forecasting accuracy.
We extend Bayes' theorem for upper probabilities considering likelihood uncertainty.
We give an exposition and numerical studies of upper hedging prices in multinomial models from the viewpoint of linear programming and the game-theoretic probability of Shafer and Vovk. We also show that, as the number of rounds goes to infinity, the upper hedging price of a European option converges to the solution of…
New method improves understanding of machine learning model performance.
FLAIR measures LP competitiveness in AMMs, improving LP performance evaluations.
Upper bound for Hausdorff distance between hyperbolic space and its medianization.
Knotted ribbons form an important topic in knot theory. They have applications in natural sciences, such as cyclic duplex DNA modeling. A flat knotted ribbon can be obtained by gently pulling a knotted ribbon tight so that it becomes flat and folded. An important problem in knot theory is to study the minimal ratio of …
Study calculates mass of special polyhedra in hyperbolic space.
New bounds on AE success probability in GP models.
Upper bound for Laplacian eigenvalue via conformal volume.
Improves conditional coverage of regression models using conformal prediction.
Upper bounds for volume spectrum depend on volume, dimension, and a conformal invariant.
Neural networks with rectified linear unit activations are essentially multivariate linear splines. As such, one of many ways to measure the "complexity" or "expressivity" of a neural network is to count the number of knots in the spline model. We study the number of knots in fully-connected feedforward neural networks…
The paper bounds the mean absolute error in DNN vector-to-vector regression.
Three models are shown to be isometrically equivalent, with a gapless first eigenvalue.
We show that any space with a positive upper curvature bound has in a small neighborhood of any point a closely related metric with a negative upper curvature bound.
Improved regret bounds for bandits with expert advice.
Sharp upper bounds found for Steklov eigenvalues of a specific hypersurface.
Study proves upper bounds for solutions on Riemannian manifolds.
Researchers find a way to price American options without relying on specific asset price models.
In a discrete-time financial market, a generalized duality is established for model-free superhedging, given marginal distributions of the underlying asset. Contrary to prior studies, we do not require contingent claims to be upper semicontinuous, allowing for upper semi-analytic ones. The generalized duality stipulate…
New algorithm reduces regret and constraint violation in adversarial CMDP learning.
New upper bound for Neumann Laplacian eigenvalues on convex domains.
Upper bounds for Steklov eigenvalues derived from intersection indices.
Develops local population-risk certificates for model updates
Upper bound on geodesic ball volume in Riemannian manifolds.
We obtain upper bounds for the eigenvalues of the Schrödinger operator depending on integral quantities of the potential and a conformal invariant called the min-conformal volume. Moreover, when the Schrödinger operator is positive, integral quantities of which appear in upper bounds, can be repla…