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.
Active-set algorithm improves Cox regression for shape-restricted covariates.
We consider the problem of nonparametric regression under shape constraints. The main examples include isotonic regression (with respect to any partial order), unimodal/convex regression, additive shape-restricted regression, and constrained single index model. We review some of the theoretical properties of the least …
New method for surface analysis using restricted deformation bases.
We analyze a monetary system of random money transfer on the basis of double entry bookkeeping. Without boundary conditions, we do not reach a price equilibrium and violate text-book formulas of economists quantity theory (MV=PQ). To match the resulting quantity of money with the model assumption of a constant price, w…
Classifies shapes of yield curves in the Svensson family.
A new shape space allows optimization of non-smooth shapes in fluid mechanics.
The article derives integral formulas for foliated sub-Riemannian manifolds.
We investigate the submanifold geometry of the orbits of Hermann actions on Riemannian symmetric spaces. After proving that the curvature and shape operators of these orbits commute, we calculate the eigenvalues of the shape operators in terms of the restricted roots. As applications, we get a formula for the volumes o…
We use matrix iteration theory to characterize acceleration in smooth games. We define the spectral shape of a family of games as the set containing all eigenvalues of the Jacobians of standard gradient dynamics in the family. Shapes restricted to the real line represent well-understood classes of problems, like minimi…
New adaptive test for NPIV models controls size and has superior power.
New scheme optimizes BMI through probabilistic and geometric shaping.
Design-by-Morphing creates radical airfoil designs without geometric constraints.
Describing shapes by suitable measures in object segmentation, as proposed in [24], allows to combine the advantages of the representations as parametrized contours and indicator functions. The pseudo-Riemannian structure of optimal transport can be used to model shapes in ways similar as with contours, while the Kanto…
Kernel-based tests for shape constraints in finance.
Simplifies Wulff theorem for crystalline shapes using Minkowski Theory.
Paper tackles hard shape constraints in kernel machines.
Let be a most singular orbit of the isotropy representation of a simple symmetric space. Let be an irreducible factor of the normal holonomy representation . We prove that there exists a basis of a section of such that the corresponding shape operators have rational…
New method uses KL-divergence to create non-informative priors for multivariate Gaussian.
Many procedures in science, engineering and medicine produce data in the form of geometric shapes. Mathematically, a shape can be modeled as an un-parameterized immersed sub-manifold, which is the notion of shape used here. Endowing shape space with a Riemannian metric opens up the world of Riemannian differential geom…
This paper restricts efficient geodesics to non-separating curves.
Given a set of points that sample a shape, the Rips complex of the data points is often used in machine-learning to provide an approximation of the shape easily-computed. It has been proved recently that the Rips complex captures the homotopy type of the shape assuming the vertices of the complex meet some mild samplin…
Optimizing fluid-dynamic performance is an important engineering task. Traditionally, experts design shapes based on empirical estimations and verify them through expensive experiments. This costly process, both in terms of time and space, may only explore a limited number of shapes and lead to sub-optimal designs. In …
New method generates optical vortices in any knot shape.
Study finds a non-locally contractible -convex set.
Given a polyhedral surface, assume that it is prohibited to change the shape and size of any face but it is permissible to change the dihedral angles between the faces. A polyhedral surface is said to be flexible if it is possible to change its shape under the above restrictions. We prove that flexible polyhedral surfa…
The paper uses deep neural networks to estimate economic models without separability restrictions.
MOPI optimizes flexible set-valued mappings to achieve superior shape adaptivity in conformal prediction.
Since the pioneering work of Canham and Helfrich, variational formulations involving curvature-dependent functionals, like the classical Willmore functional, have proven useful for shape analysis of biomembranes. We address minimizers of the Canham-Helfrich functional defined over closed surfaces enclosing a fixed volu…
Paper presents an efficient algorithm for estimating Lipschitz functions from noisy data.
The paper explores a new method for landmark matching using sub-Riemannian geometry and neural networks.
Optimizes hydrokinetic turbine design using morphing and Bayesian optimization.
The volume distance from a point p to a convex hypersurface M of the (N+1)-dimensional space is defined as the minimum (N+1)-volume of a region bounded by M and a hyperplane H through the point. This function is differentiable in a neighborhood of M and if we restrict its hessian to the minimizing hyperplane H(p) we ob…
Disentangled representation learning finds compact, independent and easy-to-interpret factors of the data. Learning such has been shown to require an inductive bias, which we explicitly encode in a generative model of images. Specifically, we propose a model with two latent spaces: one that represents spatial transform…
In this paper we provide a framework for the study of isoperimetric problems in finitely generated group, through a combinatorial study of universal covers of compact simplicial complexes. We show that, when estimating filling functions, one can restrict to simplicial spheres of particular shapes, called "round" and "u…
New MMM captures hierarchical marketing effects and sign restrictions.
Homotopy theory of differentiable sheaves connects manifold properties to underlying homotopy types.
We consider a Canham-Helfrich-type variational problem defined over closed surfaces enclosing a fixed volume and having fixed surface area. The problem models the shape of multiphase biomembranes. It consists of minimizing the sum of the Canham-Helfrich energy, in which the bending rigidities and spontaneous curvatures…
Let (P1) be certain elliptic free-boundary problem on a Riemannian manifold (M,g). In this paper we study the restrictions on the topology and geometry of the fibres (the level sets) of the solutions f to (P1). We give a technique based on certain remarkable property of the fibres (the analytic representation property)…
Proves inequality for special 3D shapes, generalizing to non-symmetric ones.
In regression tasks, aleatoric uncertainty is commonly addressed by considering a parametric distribution of the output variable, which is based on strong assumptions such as symmetry, unimodality or by supposing a restricted shape. These assumptions are too limited in scenarios where complex shapes, strong skews or mu…
Recent work on single-view 3D reconstruction shows impressive results, but has been restricted to a few fixed categories where extensive training data is available. The problem of generalizing these models to new classes with limited training data is largely open. To address this problem, we present a new model archite…
Paper analyzes iterative learning for concept classes and learns half-spaces.
See http://www.youtube.com/watch?v=izbGXdjvK_I for a YouTube video showing part of the results in this paper.We will consider surfaces whose mean curvature at a point is a linear function of the square of the distance from that point to the vertical axis. We restrict ourselves here to surfaces which are cylinders over …
Study on closed curves on negatively curved surfaces, linking number formula, and restrictions.
New algorithm tackles stochastic bandits with varying arm-dependent delays.
Proposes Constrained Q-learning for reinforcement learning with constraints.
Proposes adaptive ridge regression for functional linear models with piecewise shapes.
Study minimax risk of score estimation for log-concave distributions.