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.
Global and local estimates for a curvature equation on manifolds with boundary.
Global optimization in Bayesian inference yields little additional benefit.
Continuing our previous work (arXiv:1509.07981v1), we derive another global gradient estimate for positive functions, particularly for positive solutions to the heat equation on finite or locally finite graphs. In general, the gradient estimate in the present paper is independent of our previous one. As applications, i…
Paper solves a long-standing problem with curvature estimates.
Proposes glocal hypergradient estimation for hyperparameter optimization.
New method for private density estimation of high-dimensional Gaussian mixtures.
We improve Gross-Wilson's local estimates to global ones. As an application, we study the blow-up limits of the degenerating Calabi-Yau metrics on singular fibers.
Global neural networks improve financial forecasting accuracy with larger, diverse datasets.
Non-parametric estimation of a multivariate density estimation is tackled via a method which combines traditional local smoothing with a form of global smoothing but without imposing a rigid structure. Simulation work delivers encouraging indications on the effectiveness of the method. An application to density-based c…
HALO uses local Lipschitz constants to optimize functions efficiently.
Study extends convexity in curved spaces using fractional integrals.
Extends global stability of Minkowski spacetime to minimal decay assumptions.
Analyzes error sources in global feature effect estimation methods.
Robust estimation methods find global minima efficiently via quasi-gradients.
In the first part, we derive a sharp gradient estimate for the log of Dirichlet heat kernel and Poisson heat kernel on domains, and a sharpened local Li-Yau gradient estimate that matches the global one. In the second part, without explicit curvature assumptions, we prove a global upper bound for the fundamental soluti…
We relate two notions of local error for integration schemes on Riemannian homogeneous spaces, and show how to derive global error estimates from such local bounds. In doing so, we prove for the first time that the Lie-Butcher theory of Lie group integrators leads to global error estimates.
Global gradient estimates for Fisher-KPP equation on Finsler metric measure spaces.
In this article we prove a family of local (in time) weighted Strichartz estimates with derivative losses for the Klein-Gordon equation on asymptotically de Sitter spaces and provide a heuristic argument for the non-existence of a global dispersive estimate on these spaces. The weights in the estimates depend on the ma…
The paper provides global optimization algorithms for two particularly difficult nonconvex problems raised by hybrid system identification: switching linear regression and bounded-error estimation. While most works focus on local optimization heuristics without global optimality guarantees or with guarantees valid only…
The global financial system is highly complex, with cross-border interconnections and interdependencies. In this highly interconnected environment, local financial shocks and events can be easily amplified and turned into global events. This paper analyzes the dependencies among nearly 4,000 stocks from 15 countries. T…
First order methods can take extremely long to find global minima of non-convex functions.
In this paper, we propose a distributed off-policy actor critic method to solve multi-agent reinforcement learning problems. Specifically, we assume that all agents keep local estimates of the global optimal policy parameter and update their local value function estimates independently. Then, we introduce an additional…
Bayesian optimization improves efficiency with semi-supervised learning.
This paper deals with global asymptotic stability of prolongations of flows induced by specific vector fields and their prolongations. The method used is based on various estimates of the flows.
Postprocessing reduces Bayesian optimization steps for global optima.
Estimates neural representation dimensionality from small sample sizes.
Clustering stocks reduces estimation error in global minimum variance portfolio.
The paper provides gradient estimates for solutions on manifolds with integral Ricci bounds.
The study analyzes how covariance estimation errors affect the global minimum-variance portfolio under heavy-tailed distributions.
Develops a computationally tractable high-dimensional differential privacy estimator.
Financial global crisis has devastating impacts to economies since early XX century and continues to impose increasing collateral damages for governments, enterprises, and society in general. Up to now, all efforts to obtain efficient methods to predict these events have been disappointing. However, the quest for a rob…
The problem of estimation error of Expected Shortfall is analyzed, with a view of its introduction as a global regulatory risk measure.
The paper studies minimal surface flow and translating solitons, proving global solutions and convergence.
This paper explores estimating chaotic dynamics and parameters using local ensemble Kalman filters.
Paper proves a quantitative estimate for transforming almost complex structures into standard ones.
Unified framework for global and local two-sample conditional distribution testing.
Survey of global Calderón-Zygmund inequalities on Riemannian manifolds.
Mixed membership factorization is a popular approach for analyzing data sets that have within-sample heterogeneity. In recent years, several algorithms have been developed for mixed membership matrix factorization, but they only guarantee estimates from a local optimum. Here, we derive a global optimization (GOP) algor…
We derive global estimates in critical scale invariant norms for solutions of elliptic systems with antisymmetric potentials and almost holomorphic Hopf differential in two dimensions. Moreover we obtain new energy identities in such norms for sequences of solutions of these systems. The results apply to harmonic maps …
Prove a global shadow lemma for Patterson-Sullivan measures associated with relatively Morse subgroups in higher-rank semisimple Lie groups.
This paper presents foundational theoretical results on distributed parameter estimation for undirected probabilistic graphical models. It introduces a general condition on composite likelihood decompositions of these models which guarantees the global consistency of distributed estimators, provided the local estimator…
New method improves MMD estimation without convexity assumptions.
In this paper is proposed a new heuristic approach belonging to the field of evolutionary Estimation of Distribution Algorithms (EDAs). EDAs builds a probability model and a set of solutions is sampled from the model which characterizes the distribution of such solutions. The main framework of the proposed method is an…
Global fixed income returns span across multiple maturities and economies, that is, they naturally reside on multi-dimensional data structures referred to as tensors. In contrast to standard "flat-view" multivariate models that are agnostic to data structure and only describe linear pairwise relationships, we introduce…
Non-convex regularizers usually improve the performance of sparse estimation in practice. To prove this fact, we study the conditions of sparse estimations for the sharp concave regularizers which are a general family of non-convex regularizers including many existing regularizers. For the global solutions of the regul…
HNPE uses auxiliary data to estimate parameters in uncertain models.
JSRT improves regression tree performance by incorporating global node information.
Paper improves a method for fast global and local convergence in optimization.