arXiv research

A new method for incorporating preferences in multi-objective Bayesian optimization.

Proposes a deep neural network for multi-dimensional functional data classification.

In this contribution we describe an approach to evolve composite covariance functions for Gaussian processes using genetic programming. A critical aspect of Gaussian processes and similar kernel-based models such as SVM is, that the covariance function should be adapted to the modeled data. Frequently, the squared expo…

In this paper, a new approach of defining Steiner symmetrization of coercive convex functions is proposed and some fundamental properties of the new Steiner symmetrization are proved. Further, using the new Steiner symmetrization, we give a different approach to prove a functional version of the Blaschke-Santalo inequa…

A novel dictionary-based approach for predicting functions.

New method optimizes portfolio weights as functions, outperforming traditional approaches.

Functional approach calculates path probabilities in stochastic motion.

In binary classification problems, mainly two approaches have been proposed; one is loss function approach and the other is uncertainty set approach. The loss function approach is applied to major learning algorithms such as support vector machine (SVM) and boosting methods. The loss function represents the penalty of …

Karcher reimagined elliptic functions using geometry.

Proposes a method to estimate functional graphical models from multivariate random functions.

New method groups similar functional covariates for better modeling.

A novel likelihood function for MRFs approximates marginal likelihoods and uses copulas to reconstruct the joint likelihood.

Study proposes a functional for LCK metrics on complex manifolds.

An adaptive dropout approach improves high-dimensional Bayesian optimization.

Unified framework for Bayesian PDE-constrained inversion using physics-informed neural networks.

We use a semisupervised learning algorithm based on a topological data analysis approach to assign functional categories to yeast proteins using similarity graphs. This new approach to analyzing biological networks yields results that are as good as or better than state of the art existing approaches.

The study proves a quantitative functional CLT for neural networks with smooth activation functions.

Novel Bayesian Optimization for cost-effective function evaluation.

Bayesian optimization for composite functions improves efficiency.

Optimal unimodal fitting for linear loss functions in a sequential, efficient manner.

We discuss the nature of structure-preserving maps of varies function algebras. In particular, we identify isomorphisms between special Colombeau algebras on manifolds with invertible manifold-valued generalized functions in the case of smooth parametrization. As a consequence, and to underline the consistency and vali…

New approach for neural models to be transparent over structured data.

A simpler binary hashing approach improves precision and recall.

Statistical approaches for Functional Data Analysis concern the paradigm for which the individuals are functions or curves rather than finite dimensional vectors. In this paper, we particularly focus on the modeling and the classification of functional data which are temporal curves presenting regime changes over time.…

Develops a functional mix-of-experts model for multiclass classification.

Quantum approach models economic decisions with probabilistic and dynamic probabilities.

The problem of multilabel classification when the labels are related through a hierarchical categorization scheme occurs in many application domains such as computational biology. For example, this problem arises naturally when trying to automatically assign gene function using a controlled vocabularies like Gene Ontol…

The paper proposes methods to find a shared active subspace for multivariate vector-valued functions.

A new method uses optimal transport for imitation learning, improving efficiency and performance.

A new batch Bayesian optimization method for less expensive functions.

Discrete approach proves a new version of Wirtinger's inequality.

New method optimizes unknown functions adaptively.

New approach to time functions on spacetimes using attractors.

This paper extends semi-structured networks to functional data.

Enhanced tree-based classifiers use derivatives and geometry for better function classification.

Gradient-based clustering method for various cost functions.

Unified theory for representation learning using learnable functions.

Derives an explicit formula for optimal portfolios in financial markets.

New method approximates Gaussian curvature on discrete surfaces.

Faster algorithms solve convex function learning problems.

Bayesian optimization uses acquisition functions to find optimal solutions efficiently.

New simulations advise caution in choosing principal components for multivariate functional data.

New approach solves utility maximization problems using Delta family.

A new decoupled approach for Gaussian processes reduces complexity and improves performance.

Unified approach for nonparametric regression and conditional distribution learning.

New approach to bilevel optimization for machine learning using functional methods.

Unified approach for predicting missing segments in partially observed functions.

Efficient binary sampling method for global optimization of univariate functions with low regret.