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.
The paper tackles decision making problems with funnel structure in email marketing campaigns.
Study constructs non-funnel foliations in 3D manifolds.
Improves classifier performance in multi-stage selection processes.
A new layer, funnel, reduces dimensionality in flows for better performance.
Funnel-Transformer reduces computation by compressing sequence data.
Paper uses Shapley values to identify key confounders in product funnel data.
We establish a sharp geometric constant for the upper bound on the resonance counting function for surfaces with hyperbolic ends. An arbitrary metric is allowed within some compact core, and the ends may be of hyperbolic planar, funnel, or cusp type. The constant in the upper bound depends only on the volume of the cor…
Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and doing so more accurately than when naively classifying each document via its corresponding language-specific classifier. In order to obtain an…
Descending phase retrieval algorithms show a phase transition with increasing sample complexity.
In this paper we prove that any Riemannian surface, with no restriction of curvature at all, can be decomposed into blocks belonging just to some of these types: generalized Y-pieces, generalized funnels and halfplanes.
WALNUTS improves sampling efficiency and robustness for multi-scale distributions.
Multidimensional time series are sequences of real valued vectors. They occur in different areas, for example handwritten characters, GPS tracking, and gestures of modern virtual reality motion controllers. Within these areas, a common task is to search for similar time series. Dynamic Time Warping (DTW) is a common di…
Improves classifier performance in multi-stage processes with adversarial autoencoders and multi-task learning.
Adaptive-stepsize MCMC sampling inspired by Adam optimizer.
Improved MUSE boosts performance and reduces error in Bayesian inference.
This paper solves the dual Minkowski problem for q-torsional rigidity.
Researchers prove uniqueness and continuity of solution to L_p dual Minkowski problem.
Solves a generalized dual Minkowski problem for specific values of q.
Dual martingales improve primal optimal stopping problem efficiency.
Study anisotropic inverse Gauss curvature flows and solve dual Orlicz Minkowski problems.
In this paper, the dual Orlicz curvature measure is proposed and its basic properties are provided. A variational formula for the dual Orlicz-quermassintegral is established in order to give a geometric interpretation of the dual Orlicz curvature measure. Based on the established variational formula, a solution to the …
On compact surfaces with or without boundary, Osgood, Phillips and Sarnak proved that the maximum of the determinant of the Laplacian within a conformal class of metrics with fixed area occurs at a metric of constant curvature and, for negative Euler characteristic, exhibited a flow from a given metric to a constant cu…
Efficient algorithm solves best subset selection problem.
Paper solves dual Minkowski problem in 2D plane for specific curvature cases.
Optimizes subset selection in sparse learning problems.
We study projectively self-dual polygons and curves in the projective plane. Our results provide a partial answer to problem No 1994-17 in the book of Arnold's problems.
Study improves cryptocurrency price prediction using unlabeled text data.
This paper discusses the numéraire-based utility maximization problem in markets with proportional transaction costs. In particular, the investor is required to liquidate all her position in stock at the terminal time. We first observe the stability of the primal and dual value functions as well as the convergence of t…
We consider empirical risk minimization of linear predictors with convex loss functions. Such problems can be reformulated as convex-concave saddle point problems, and thus are well suitable for primal-dual first-order algorithms. However, primal-dual algorithms often require explicit strongly convex regularization in …
Study anisotropic flows without global terms and solve dual Orlicz Christoffel-Minkowski problems.
We introduce the dual isoperimetrix which solves the isoperimetric problem in the dual Brunn-Minkowski theory. We then show how the dual isoperimetrix is related to the isoperimetrix from the Brunn-Minkowski theory.
Study projective deformations of hyperbolic 3-orbifolds with turnover ends.
New method solves a generalized Minkowski problem using a curvature flow.
Derives stability for curvature measure near constant density, proving dual Minkowski problem solutions.
Paper offers a dual formulation for consumption problem with multiplicative habit.
Study investigates duality and dual optimizers for various transport problems.
Derives a primal-dual MLSVD formulation for multilinear data.
This work studies the strong duality of non-convex matrix factorization problems: we show that under certain dual conditions, these problems and its dual have the same optimum. This has been well understood for convex optimization, but little was known for non-convex problems. We propose a novel analytical framework an…
Previous studies on stochastic primal-dual algorithms for solving min-max problems with faster convergence heavily rely on the bilinear structure of the problem, which restricts their applicability to a narrowed range of problems. The main contribution of this paper is the design and analysis of new stochastic primal-d…
Given a convex optimization problem and its dual, there are many possible first-order algorithms. In this paper, we show the equivalence between mirror descent algorithms and algorithms generalizing the conditional gradient method. This is done through convex duality, and implies notably that for certain problems, such…
Smooth even solutions found for a generalized convex geometry problem.
Drago optimizes DRO problems with faster convergence.
Sketching techniques have become popular for scaling up machine learning algorithms by reducing the sample size or dimensionality of massive data sets, while still maintaining the statistical power of big data. In this paper, we study sketching from an optimization point of view: we first show that the iterative Hessia…
The general volume of a star body, a notion that includes the usual volume, the th dual volumes, and many previous types of dual mixed volumes, is introduced. A corresponding new general dual Orlicz curvature measure is defined that specializes to the -dual curvature measures introduced recently by Lutwak, Ya…
Study confirms the uniqueness of the unit sphere for a specific geometric problem.
In this paper, we study a constrained utility maximization problem following the convex duality approach. After formulating the primal and dual problems, we construct the necessary and sufficient conditions for both the primal and dual problems in terms of FBSDEs plus additional conditions. Such formulation then allows…
We propose a dynamical theory of market liquidity that predicts that the average supply/demand profile is V-shaped and {\it vanishes} around the current price. This result is generic, and only relies on mild assumptions about the order flow and on the fact that prices are (to a first approximation) diffusive. This natu…
In the present paper, the primal-dual problem consisting of the investment risk minimization problem and the expected return maximization problem in the mean-variance model is discussed using replica analysis. As a natural extension of the investment risk minimization problem under only a budget constraint that we anal…