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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.

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35810 · Mar 202619922001200920172026
48 results for Aitchison simplex

PolyILR: A Tree-Structured Orthonormal Decomposition of Compositional Data

problem Representing compositional data with hierarchical structure
method PolyILR: A canonical orthonormal decomposition of the Aitchison tangent space aligned with any tree topology
result PolyILR yields stable, interpretable features and enables inference at multiscale tree resolution

Geometry-aware KDE model improves multiclass quantification.

problem Accurately estimating class prevalence for label shift adaptation.
method Log-ratio representations and Aitchison geometry for compositional data, shrinkage regularization.
result Competitive with state-of-the-art quantifiers, often improving over standard KDE-based baselines.

New method simulates multivariate extreme events using GANs and Aitchison coordinates.

problem Simulating multivariate extreme events for economic risk assessment.
method Wasserstein-Aitchison GAN approach combining tail dependence and marginal tail modeling.
result Strong performance in capturing tail dependence and generating accurate extreme observations.

Aitchison and Rubinstein constructed two knot complements that can be decomposed into two regular ideal dodecahedra. This paper shows that these knot complements are the only knot complements that decompose into n regular ideal dodecahedra, providing a partial solution to a conjecture of Neumann and Reid.

2012-09-05abs ↗pdf ↗

The paper triangulates Heisenberg groups with horizontal and straight simplexes.

problem Triangulating Heisenberg groups with specific regularity properties.
method Constructing triangulations with horizontal and straight simplexes on a polyhedral structure and extending to the whole Heisenberg group.
result Explicit examples of grid and triangulations provided.

Study optimal transport on simplex boundary, proving transport map and potential regularity.

problem Regularity of transport map and potential on simplex boundary.
method Boundary regularity results for optimal transport maps, exploiting simplex symmetries.
result Regularity properties of transport map and its convex potential.

The Bezier simplex fitting is a novel data modeling technique which exploits geometric structures of data to approximate the Pareto front of multi-objective optimization problems. There are two fitting methods based on different sampling strategies. The inductive skeleton fitting employs a stratified subsampling from e…

2019-06-17abs ↗pdf ↗

A hyperbolic 3-simplex reflection group is a Coxeter group arising as a lattice in the isometry group of hyperbolic 3-space, with fundamental domain a geodesic simplex (possibly with some ideal vertices). The classification of these groups is known, and there are exactly 9 cocompact examples, and 23 non-cocompact examp…

2007-05-07abs ↗pdf ↗

We introduce the non-pure versions of simplicial balls and spheres with minimum number of vertices. These are a special type of non-homogeneous balls and spheres (NH-balls and NH-spheres) satisfying a minimality condition on the number of maximal simplices. The main result is that minimal NH-balls and NH-spheres are pr…

2014-06-25abs ↗pdf ↗

In 1973, J. Cheeger and J. Simons raised the following question that still remains open and is known as the Rational Simplex Problem: Given a geodesic simplex in the spherical 3-space so that all of its interior dihedral angles are rational multiples of ππ, is it true that its volume is a rational multiple of the volu…

2013-04-28abs ↗pdf ↗

A faster Wasserstein k-means algorithm for histogram data reduces computation and maintains clustering quality.

problem Efficiently clustering histogram data with reduced computation time.
method Sparse simplex projection to reduce data samples, centroids, and ground cost matrix, dynamically removing lower-valued samples.
result Significant reduction in computational complexity without compromising clustering quality.

Proposes an accuracy-preserving calibration method for DNNs.

problem Calibration of deep neural networks (DNNs) to measure prediction reliability.
method Uses Concrete distribution on the probability simplex to calibrate DNNs without accuracy loss.
result The proposed method outperforms previous methods in accuracy-preserving calibration tasks.

On the probability simplex, we can consider the standard information geometric structure with the e- and m-affine connections mutually dual with respect to the Fisher metric. The geometry naturally defines submanifolds simultaneously autoparallel for the both affine connections, which we call {\em doubly autoparallel s…

2017-11-30abs ↗pdf ↗

The paper is devoted to modeling optimal exercise strategies of the behavior of investors and issuers working with convertible bonds. This implies solution of the problems of stock price modeling, payoff computation and min-max optimization. Stock prices (underlying asset) were modeled under the assumption of the geome…

2007-10-01abs ↗pdf ↗

We show an efficient algorithm for the following problem: Given uniformly random points from an arbitrary n-dimensional simplex, estimate the simplex. The size of the sample and the number of arithmetic operations of our algorithm are polynomial in n. This answers a question of Frieze, Jerrum and Kannan [FJK]. Our resu…

2012-11-09abs ↗pdf ↗

Algorithm learns latent simplex from perturbed points in input-sparsity time.

problem Learning a latent kk-vertex simplex from noisy data.
method Input-sparsity time algorithm using low-rank approximation and adaptive selection.
result Algorithm achieves O(extrmnnz(A))O( extrm{nnz}(A)) time complexity, avoiding kextrmnnz(A)k\cdot extrm{nnz}(A).

New framework estimates staged tree models using hierarchical clustering on the probability simplex.

problem Estimating staged tree models with context-specific dependencies.
method Hierarchical clustering on the probability simplex, using simplex-based divergences and linkage methods.
result Total Variation divergence with Ward.D2 linkage produces staged trees with better model fit, structure recovery, and computational efficiency.

CAST predicts distribution-valued time series by stabilizing and transporting simplex-supported successors.

problem Forecasting distribution-valued time series with structural failure modes.
method CAST (Causal Anchored Simplex Transport) uses successors retrieved from causal context, stabilized with a persistence anchor, and locally transported on ordered supports.
result CAST outperforms baselines on eleven public and simulated benchmarks, achieving best average rank on both one-step KL and autoregressive rollout JSD.

A group action H on X is called "telescopic" if for any finitely presented group G, there exists a subgroup H' in H such that G is isomorphic to the fundamental group of X/H'. We construct examples of telescopic actions on some CAT[-1] spaces, in particular on 3 and 4-dimensional hyperbolic spaces. As applications we g…

2011-04-25abs ↗pdf ↗

Investigates VaR behavior for sums of one-sided random variables, showing impossibilities and conditions for super-additivity.

problem Investigates the behavior of Value-at-Risk (VaR) for sums of one-sided random variables.
method Analyzes the extremal aggregation behavior of VaR, introduces structural conditions for super-additivity.
result Characterizes when VaR is fully super-additive and provides unified framework for various dependence structures.

We study a natural intrinsic definition of geometric simplices in Riemannian manifolds of arbitrary dimension nn, and exploit these simplices to obtain criteria for triangulating compact Riemannian manifolds. These geometric simplices are defined using Karcher means. Given a finite set of vertices in a convex set on t…

2014-06-14abs ↗pdf ↗

Paper studies weighted Fermat-Frechet problem for simplex edge lengths.

problem Finding optimal edge lengths for simplex deformations.
method Isometric embedding techniques for KK-Space.
result New variational method to solve weighted Fermat-Frechet problem.

Researchers correct earlier work on surgeries of Gieseking's hyperbolic simplex manifold.

problem Incorrectly identified Gieseking's manifold as orbifolds, leading to a conflict with known theorems.
method Revised and completed the analysis of Dehn surgeries on Gieseking's manifold, identifying them as cone manifolds.
result Corrected the understanding of Gieseking's manifold, identifying it as cone manifolds and derived new orbifold series.

Study shows asymptotic behavior of metric near singular points of a Monge-Ampère equation.

problem Analyzing singularities of a metric defined by a Monge-Ampère equation.
method Using the tropical Monge-Ampère equation and asymptotic analysis.
result The solution is not C1,1C^{1,1} across singular points and asymptotic to the Gross-Wilson metric.

Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a popular method for scalable Bayesian inference. These methods are based on sampling a discrete-time approximation to a continuous time process, such as the Langevin diffusion. When applied to distributions defined on a constrained space the time-discret…

2018-06-19abs ↗pdf ↗

Neural networks exhibit simplex symmetry in their final and penultimate layers.

problem Understanding the symmetry in neural network layers.
method Analytical and numerical studies of toy models and deep neural networks.
result Neural networks map data points from the same class to a single point in a high-dimensional space, forming a simplex.

Log-concavity proven for multinomial likelihoods under specific constraints.

problem Log-concavity of multinomial likelihoods under interval censoring constraints.
method Proved log-concavity by showing M-convex subsets of the discrete simplex.
result Likelihood function is completely log-concave.