arXiv research

Global methods outperform local in forecasting groups of time series, even in heterogeneous datasets.

Language is dynamic, constantly evolving and adapting with respect to time, domain or topic. The adaptability of language is an active research area, where researchers discover social, cultural and domain-specific changes in language using distributional tools such as word embeddings. In this paper, we introduce the gl…

Improves global counterfactual explanations for model recourse.

The paper guarantees global stability for stochastic subgradient methods in nonsmooth nonconvex optimization.

Develops methods to calculate global index of real polynomials.

New quasi-Newton method guarantees global superlinear convergence.

First order methods can take extremely long to find global minima of non-convex functions.

Defines globalness measure for explainers using optimal transport.

Policy gradient methods with actor-critic schemes demonstrate tremendous empirical successes, especially when the actors and critics are parameterized by neural networks. However, it remains less clear whether such "neural" policy gradient methods converge to globally optimal policies and whether they even converge at …

It has been shown that deep neural networks (DNNs) may be vulnerable to adversarial attacks, raising the concern on their robustness particularly for safety-critical applications. Recognizing the local nature and limitations of existing adversarial attacks, we present a new type of global adversarial attacks for assess…

GADGET framework decomposes global feature effects using recursive partitioning.

Paper improves a method for fast global and local convergence in optimization.

In this paper, a new sequential surrogate-based optimization (SSBO) algorithm is developed, which aims to improve the global search ability and local search efficiency for the global optimization of expensive black-box models. The proposed method involves three basic sub-criteria to infill new samples asynchronously to…

Improved portfolio optimization method yields better risk-adjusted returns.

Paper proposes a quasi-Newton method for nonlinear equations with global convergence guarantees.

Global harmonic maps into SU(1,1) constructed from Smyth potentials using DPW method.

Paper develops NPG for risk-averse RL with ECRMs, proving global convergence.

ProGO optimizes non-convex functions without gradients, outperforming existing methods.

Paper solves globally optimal k-means for low dimensional data.

Purpose: Lung nodules have very diverse shapes and sizes, which makes classifying them as benign/malignant a challenging problem. In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the shape and size of a nodule using a global feature extractor, as well as …

SGD methods fail to converge to global minimizers in deep neural networks with ReLU activation.

Accelerates convergence in global non-convex optimization with reversible diffusion.

Postprocessing reduces Bayesian optimization steps for global optima.

Global optimization finds applications in a wide range of real world problems. The multi-start methods are a popular class of global optimization techniques, which are based on the ideas of conducting local searches at multiple starting points. In this work we propose a new multi-start algorithm where the starting poin…

GLOBE-CE offers efficient global counterfactual explanations.

The restricted planar three-body problem has a rich history, yet many unanswered questions still remain. In the present paper we prove the existence of a global surface of section near the smaller body in a new range of energies and mass ratios for which the Hill's region still has three connected components. The appro…

JSRT improves regression tree performance by incorporating global node information.

New DR algorithm preserves both local and global structure.

Gradient descent finds global optima in ResNets with sufficient parameters.

Global constraints and reranking have not been used in cognates detection research to date. We propose methods for using global constraints by performing rescoring of the score matrices produced by state of the art cognates detection systems. Using global constraints to perform rescoring is complementary to state of th…

The exactness equation for Lepage 2-forms, associated with variational systems of ordinary differential equations on smooth manifolds, is analyzed with the aim to construct a concrete global variational principle. It is shown that locally variational systems defined by homogeneous functions of degree $c \neq 0, 1$ are …

Dropout is used to avoid overfitting by randomly dropping units from the neural networks during training. Inspired by dropout, this paper presents GI-Dropout, a novel dropout method integrating with global information to improve neural networks for text classification. Unlike the traditional dropout method in which the…

We consider the problem of analyzing the heterogeneity of clustering distributions for multiple groups of observed data, each of which is indexed by a covariate value, and inferring global clusters arising from observations aggregated over the covariate domain. We propose a novel Bayesian nonparametric method reposing …

Paper proposes Meta Label Learning to infer global labels for robust few-shot models.

Bayesian optimization has recently emerged as a popular method for the sample-efficient optimization of expensive black-box functions. However, the application to high-dimensional problems with several thousand observations remains challenging, and on difficult problems Bayesian optimization is often not competitive wi…

Extends global stability of Minkowski spacetime to minimal decay assumptions.

New method uses diffusion models to speed up MCMC sampling.

WSFN overcomes saddle points for non-convex functionals in Wasserstein space.

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.

New method certifies global robustness of neural networks efficiently.

FedReLa: A novel data-level approach for imbalanced federated learning

New method R-LOCO improves local feature importance analysis.

Paper proposes quantum methods for optimizing machine learning functions.

New method calibrates eSSVI volatility surfaces without arbitrage.

New method for global optimization of Gaussian processes reduces computational time.

In this paper we study the global behavior of the Ricci flow equation for two classes of homogeneous manifolds with two isotropy summands. Using methods of the qualitative theory of differential equations, we present the global phase portrait of such systems and derive some geometrical consequences on the structure of …

Proposes glocal hypergradient estimation for hyperparameter optimization.

Unified framework for global and local two-sample conditional distribution testing.