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…

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

A novel dictionary-based approach for predicting functions.

Karcher reimagined elliptic functions using geometry.

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 …

Due to the intractable partition function, the exact likelihood function for a Markov random field (MRF), in many situations, can only be approximated. Major approximation approaches include pseudolikelihood and Laplace approximation. In this paper, we propose a novel way of approximating the likelihood function throug…

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

Imitation Learning describes the problem of recovering an expert policy from demonstrations. While inverse reinforcement learning approaches are known to be very sample-efficient in terms of expert demonstrations, they usually require problem-dependent reward functions or a (task-)specific reward-function regularizatio…

New method groups similar functional covariates for better modeling.

Study proposes a functional for LCK metrics on complex manifolds.

An adaptive dropout approach improves high-dimensional Bayesian optimization.

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.

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

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

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…

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

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

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.…

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.

We consider optimization of composite objective functions, i.e., of the form $f(x)=g(h(x))$, where $h$ is a black-box derivative-free expensive-to-evaluate function with vector-valued outputs, and $g$ is a cheap-to-evaluate real-valued function. While these problems can be solved with standard Bayesian optimization, we…

New method approximates Gaussian curvature on discrete surfaces.

This paper extends semi-structured networks to functional data.

Bayesian optimization (BO) and its batch extensions are successful for optimizing expensive black-box functions. However, these traditional BO approaches are not yet ideal for optimizing less expensive functions when the computational cost of BO can dominate the cost of evaluating the blackbox function. Examples of the…

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.

Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search process. Fully maximizing acquisition functions produces the Bayes' decision rule, but this ideal is difficult to achieve since these functions …

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

Binary hashing is a well-known approach for fast approximate nearest-neighbor search in information retrieval. Much work has focused on affinity-based objective functions involving the hash functions or binary codes. These objective functions encode neighborhood information between data points and are often inspired by…

Faster algorithms solve convex function learning problems.

New approach solves utility maximization problems using Delta family.

Unified approach for nonparametric regression and conditional distribution learning.

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

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

Unified approach for predicting missing segments in partially observed functions.

We present a new mixture model-based discriminant analysis approach for functional data using a specific hidden process regression model. The approach allows for fitting flexible curve-models to each class of complex-shaped curves presenting regime changes. The model parameters are learned by maximizing the observed-da…

RSF models censored functional data for better survival analysis.

New GP model estimates piecewise continuous functions.

The paper explores how prior functions and bootstrapping improve ensemble uncertainty estimation.

The Heston model is a popular stock price model with stochastic volatility that has found numerous applications in practice. In the present paper, we study the Riemannian distance function associated with the Heston model and obtain explicit formulas for this function using geometrical and analytical methods. Geometric…

We propose a novel Bayesian Optimization approach for black-box functions with an environmental variable whose value determines the tradeoff between evaluation cost and the fidelity of the evaluations. Further, we use a novel approach to sampling support points, allowing faster construction of the acquisition function.…

Bayesian approach approximates probability functions of Gaussian mixtures.

We consider a standard optimal investment problem in a complete financial market driven by a Wiener process and derive an explicit formula for the optimal portfolio process in terms of the vertical derivative from functional It^o calculus. An advantage with this approach compared to the Malliavin calculus approach is t…