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.
Paper proposes a shape-constrained approach to distributionally robust learning.
The paper learns pose variations within shape populations using constrained mixtures of factor analyzers.
Study uses outer metrics for PDE-constrained shape optimization over diffeomorphism group.
Shape-constrained symbolic regression improves model extrapolation with prior knowledge.
Study of star-shaped hypersurfaces with capillary boundary using constrained mean curvature flow.
Functional BART adds shape priors to Bayesian tree regression for better curve fitting.
Paper tackles hard shape constraints in kernel machines.
Paper tackles shape graph registration using neural networks.
Space mapping speeds up shape optimization for PDEs.
Proposes Constrained Q-learning for reinforcement learning with constraints.
The paper improves GP regression for sparse sensor data in structural mode shape reconstruction.
Study shows limits of volume-constrained sets are finite unions of Wulff shapes.
New algorithms reduce rejection sampling complexity for shape-constrained distributions.
We consider market players with tail-risk-seeking behaviour as exemplified by the S-shaped utility introduced by Kahneman and Tversky. We argue that risk measures such as value at risk (VaR) and expected shortfall (ES) are ineffective in constraining such players. We show that, in many standard market models, product d…
Shape-constrained convex regression problem deals with fitting a convex function to the observed data, where additional constraints are imposed, such as component-wise monotonicity and uniform Lipschitz continuity. This paper provides a comprehensive mechanism for computing the least squares estimator of a multivariate…
HyCNNs improve convex function learning and optimal transport.
We solve S-shaped utility portfolio selection with SD constraints using algorithms and neural networks.
Two methods are proposed for high-dimensional shape-constrained regression and classification. These methods reshape pre-trained prediction rules to satisfy shape constraints like monotonicity and convexity. The first method can be applied to any pre-trained prediction rule, while the second method deals specifically w…
A new shape space allows optimization of non-smooth shapes in fluid mechanics.
We establish existence of compact minimizers of the prescribed mean curvature problem with volume constraint in periodic media. As a consequence, we construct compact approximate solutions to the prescribed mean curvature equation. We also show convergence after rescaling of the volume-constrained minimizers towards a …
In-BO optimizes complex constrained domains using SIn-GP surrogate models.
We demonstrate how easy it is for modern machine-learned systems to violate common deontological ethical principles and social norms such as "favor the less fortunate," and "do not penalize good attributes." We propose that in some cases such ethical principles can be incorporated into a machine-learned model by adding…
Study S-shaped utility maximization with VaR constraint and unobservable drift.
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 …
VCAE improves autoencoder quality on MNIST and CelebA.
Despite remarkable advances in automated visual recognition by machines, some visual tasks remain challenging for machines. Fleuret et al. (2011) introduced the Synthetic Visual Reasoning Test (SVRT) to highlight this point, which required classification of images consisting of randomly generated shapes based on hidden…
New method reconstructs 3D shapes from 2D images using Kendall's shape space.
Gaussian processes (GPs) provide a powerful framework for extrapolation, interpolation, and noise removal in regression and classification. This paper considers constraining GPs to arbitrarily-shaped domains with boundary conditions. We solve a Fourier-like generalised harmonic feature representation of the GP prior in…
New approach shapes error distribution in long-term forecasting.
Develops methods for estimating constrained function-valued parameters in infinite-dimensional models.
We present new computations of tight shapes obtained using the constrained gradient descent code RIDGERUNNER for 544 composite knots with 12 and fewer crossings, expanding our dataset to 943 knots and links. We use the new data set to analyze two outstanding conjectures about tight knots, namely that the ropelengths of…
The paper introduces a new flow to converge to a Wulff shape from a smooth convex hypersurface.
A framework for generating 3D shapes by sequentially assembling primitives.
Given a matrix the seriation problem consists in permuting its rows in such way that all its columns have the same shape, for example, they are monotone increasing. We propose a statistical approach to this problem where the matrix of interest is observed with noise and study the corresponding minimax rate of estimatio…
T-Basis represents neural network tensors with fewer parameters.
Introduces Star-Shaped DDPMs for non-Gaussian distributions.
Transforms curves and surfaces for efficient geometric analysis.
Proposes a tuning-free dynamic pricing method for linear valuation models.
We consider -way data arrays and low-rank tensor factorizations where the time mode is coded as a sparse linear combination of temporal elements from an over-complete library. Our method, Shape Constrained Tensor Decomposition (SCTD) is based upon the CANDECOMP/PARAFAC (CP) decomposition which produces -rank appr…
This paper introduces constrained mixtures for continuous distributions, characterized by a mixture of distributions where each distribution has a shape similar to the base distribution and disjoint domains. This new concept is used to create generalized asymmetric versions of the Laplace and normal distributions, whic…
Given an elliptic integrand of class , we prove that finite unions of disjoint open Wulff shapes with equal radii are the only volume-constrained critical points of the anisotropic surface energy among all sets with finite perimeter and reduced boundary almost equal to its closure.
The distance of an almost constant mean curvature boundary from a finite family of disjoint tangent balls with equal radii is quantitatively controlled in terms of the oscillation of the scalar mean curvature. This result allows one to quantitatively describe the geometry of volume-constrained stationary sets in capill…
We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes. Our main contribution is the characterization of the nature of its shape: a general polytope (Aigner et al., 2010). To demonstrate this result, we exhibit several properties of the structu…
Latent class model (LCM), which is a finite mixture of different categorical distributions, is one of the most widely used models in statistics and machine learning fields. Because of its non-continuous nature and the flexibility in shape, researchers in practice areas such as marketing and social sciences also frequen…
Study proves a new formula for capillary hypersurfaces and shows a flow converging to a special shape.
We consider a singular control problem with regime switching that arises in problems of optimal investment decisions of cash-constrained firms. The value function is proved to be the unique viscosity solution of the associated Hamilton-Jacobi-Bellman equation. Moreover, we give regularity properties of the value functi…
An edge tessellation is a tiling of the plane generated by reflecting a polygon in its edges. We prove that a polygon generating an edge tessellation is one the following eight types: a rectangle; an equilateral, 60-right, isosceles right, or 120-isosceles triangle; a 120-rhombus; a 60-90-120 kite; or a regular hexagon…
The paper explores how structured representations influence learning dynamics in neural networks.