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Found 22 papers, 3 papers with code
Combining additivity and active subspaces for high-dimensional Gaussian process modeling
Gaussian processes are a widely embraced technique for regression and classification due to their good prediction accuracy, analytical tractability and built-in capabilities for uncertainty quantification.
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
Despite their many desirable properties, Gaussian processes (GPs) are often compared unfavorably to deep neural networks (NNs) for lacking the ability to learn representations.
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow.
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation
Sparse Gaussian Processes are a key component of high-throughput Bayesian Optimisation (BO) loops; however, we show that existing methods for allocating their inducing points severely hamper optimisation performance.
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning
Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning.
A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design
We present HIghly Parallelisable Pareto Optimisation (HIPPO) -- a batch acquisition function that enables multi-objective Bayesian optimisation methods to efficiently exploit parallel processing resources.
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation
By choosing inducing points to maximally reduce both global uncertainty and uncertainty in the maximum value of the objective function, we build surrogate models able to support high-precision high-throughput BO.
Revisiting Bayesian Optimization in the light of the COCO benchmark
It is commonly believed that Bayesian optimization (BO) algorithms are highly efficient for optimizing numerically costly functions.
TREGO: a Trust-Region Framework for Efficient Global Optimization
By following a classical scheme for the trust region (based on a sufficient decrease condition), the proposed algorithm enjoys global convergence properties, while departing from EGO only for a subset of optimization steps.
On Information Gain and Regret Bounds in Gaussian Process Bandits
For the Mat\'ern family of kernels, where the lower bounds on $\gamma_T$, and regret under the frequentist setting, are known, our results close a huge polynomial in $T$ gap between the upper and lower bounds (up to logarithmic in $T$ factors).
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation
Many machine learning models require a training procedure based on running stochastic gradient descent.
Scalable Thompson Sampling using Sparse Gaussian Process Models
We provide theoretical guarantees and show that the drastic reduction in computational complexity of scalable TS can be enjoyed without loss in the regret performance over the standard TS.
Bayesian Quantile and Expectile Optimisation
In this paper, we propose new variational models for Bayesian quantile and expectile regression that are well-suited for heteroscedastic noise settings.
Ordinal Bayesian Optimisation
Bayesian optimisation is a powerful tool to solve expensive black-box problems, but fails when the stationary assumption made on the objective function is strongly violated, which is the case in particular for ill-conditioned or discontinuous objectives.
Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version
Parametric shape optimization aims at minimizing an objective function f(x) where x are CAD parameters.
X-Armed Bandits: Optimizing Quantiles, CVaR and Other Risks
We finally present numerical experiments that show a dramatic impact of tight bounds for the optimization of quantiles and CVaR.
The Kalai-Smorodinski solution for many-objective Bayesian optimization
An ongoing aim of research in multiobjective Bayesian optimization is to extend its applicability to a large number of objectives.
A Review on Quantile Regression for Stochastic Computer Experiments
We report on an empirical study of the main strategies for quantile regression in the context of stochastic computer experiments.
Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Batch Versions
Multi-objective optimization aims at finding trade-off solutions to conflicting objectives.
Budgeted Multi-Objective Optimization with a Focus on the Central Part of the Pareto Front -- Extended Version
When the number of experiments is severely restricted and/or when the number of objectives increases, uncovering the whole set of Pareto optimal solutions is out of reach, even for surrogate-based approaches: the proposed solutions are sub-optimal or do not cover the front well.
A Bayesian optimization approach to find Nash equilibria
Game theory finds nowadays a broad range of applications in engineering and machine learning.
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian
An augmented Lagrangian (AL) can convert a constrained optimization problem into a sequence of simpler (e. g., unconstrained) problems, which are then usually solved with local solvers.
