arXiv research

A new method combines Gaussian Processes to optimize under uncertainty.

Optimizes Gaussian process hyperparameters using Bayesian autoregression.

New method optimizes processes under constraints using bivariate Gaussian models.

Bayesian Optimization using Gaussian Processes is a popular approach to deal with the optimization of expensive black-box functions. However, because of the a priori on the stationarity of the covariance matrix of classic Gaussian Processes, this method may not be adapted for non-stationary functions involved in the op…

Gaussian process priors are commonly used in aerospace design for performing Bayesian optimization. Nonetheless, Gaussian processes suffer two significant drawbacks: outliers are a priori assumed unlikely, and the posterior variance conditioned on observed data depends only on the locations of those data, not the assoc…

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

This thesis tackles Gaussian Process challenges in low dimensions.

Bayesian optimization sped up with scalable Gaussian processes.

Optimizes functions on manifolds using Gaussian processes and graph models.

A new method reduces energy consumption in machine learning by using multiple, less costly data sources.

New algorithms use Gaussian processes to optimize stopping times in financial markets.

The study optimizes wind farm yaw control using Gaussian process regression and high-fidelity simulations.

Combines Gaussian process and Geometric Harmonics for better uncertainty estimation.

Paper improves regret bounds for Gaussian process upper confidence bound in Bayesian optimization.

Distributed Quantum Gaussian Processes improve modeling in multi-agent systems.

The paper improves Gaussian process models for efficient batch optimization.

The paper improves Gaussian process regression by optimizing hyperparameters.

Enhanced Gaussian process models accelerate optimization and posterior approximation.

VOGP efficiently identifies Pareto optimal solutions in black-box vector optimization.

This paper optimizes Bayesian acquisition functions in Gaussian Processes for better optimization.

A new knot selection method speeds up sparse Gaussian process approximations.

Optical scatterometry is a method to measure the size and shape of periodic micro- or nanostructures on surfaces. For this purpose the geometry parameters of the structures are obtained by reproducing experimental measurement results through numerical simulations. We compare the performance of Bayesian optimization to …

Improves Bayesian optimization using Gaussian process Thompson sampling.

Gaussian processes struggle with compositional functions, but deep Gaussian processes can outperform.

Deep Gaussian Processes improve likelihood-free inference for complex distributions.

A new method uses Gaussian Processes to solve power flow problems with uncertain renewable and load inputs.

Bayesian approach uses Gaussian process for reinforcement learning.

In the last five years, the financial industry has been impacted by the emergence of digitalization and machine learning. In this article, we explore two methods that have undergone rapid development in recent years: Gaussian processes and Bayesian optimization. Gaussian processes can be seen as a generalization of Gau…

Simplified DGPs training by fixing inducing inputs to subset of data.

Efficiently searches through Gaussian process kernels using symbolic representation and Bayesian optimization.

GP CC-OPF solves uncertain power grid optimization with Gaussian Process.

Bayesian optimization on networks using Gaussian process models.

Bayesian optimization with Gaussian process as surrogate model has been successfully applied to analog circuit synthesis. In the traditional Gaussian process regression model, the kernel functions are defined explicitly. The computational complexity of training is O(N 3 ), and the computation complexity of prediction i…

Paper proposes no-regret algorithms for private GP bandit optimization.

Proposes a more efficient knot selection method for sparse Gaussian processes.

Ada-BKB optimizes black-box functions on continuous domains with adaptive discretization.

A new algorithm uses concavity in Gaussian processes to optimize decisions in bandit problems.

Optimizes black-box functions with varying costs across multiple sources.

Safe Gaussian Process Bandit Optimization with sub-linear regret bounds.

A new GP interpolation method for better predictive distributions in ranges of interest.

This paper simplifies MTGP derivations for Gaussian processes.

We apply numerical methods in combination with finite-difference-time-domain (FDTD) simulations to optimize transmission properties of plasmonic mirror color filters using a multi-objective figure of merit over a five-dimensional parameter space by utilizing novel multi-fidelity Gaussian processes approach. We compare …

Bayesian optimization is a class of global optimization techniques. In Bayesian optimization, the underlying objective function is modeled as a realization of a Gaussian process. Although the Gaussian process assumption implies a random distribution of the Bayesian optimization outputs, quantification of this uncertain…

A new method for efficient Gaussian process inference using sparse approximations.

RAMBO optimizes multi-regime problems by discovering and modeling distinct energy basins.

Combines VI and EP for better Gaussian process hyperparameter learning.

A new kernel for probability measures based on optimal transport.

A method for predicting signals on graphs using Gaussian processes and optimal transport.