arXiv research

New method optimizes black-box functions using generative models and Wasserstein distance.

Bayesian optimisation tackles expensive black-box functions with constraints.

Bayesian optimisation generates saliency maps for black-box models.

Optimising black-box functions is important in many disciplines, such as tuning machine learning models, robotics, finance and mining exploration. Bayesian optimisation is a state-of-the-art technique for the global optimisation of black-box functions which are expensive to evaluate. At the core of this approach is a G…

O3 optimizes samples from generative models without extra training.

Real world experiments are expensive, and thus it is important to reach a target in minimum number of experiments. Experimental processes often involve control variables that changes over time. Such problems can be formulated as a functional optimisation problem. We develop a novel Bayesian optimisation framework for s…

Bayesian optimisation tackles high-dimensional categorical and mixed search spaces.

Unified Bayesian Optimisation for mixed variables improves performance.

Improves Bayesian optimisation for engineering design problems with many variables.

MBORE optimizes multi-objective problems using density-ratio estimation.

Bayesian optimisation algorithm for unknown search spaces with sub-linear regret.

Many expensive black-box optimisation problems are sensitive to their inputs. In these problems it makes more sense to locate a region of good designs, than a single-possibly fragile-optimal design. Expensive black-box functions can be optimised effectively with Bayesian optimisation, where a Gaussian process is a popu…

Bayesian optimisation framework for graph functions.

Efficient optimisation of black-box problems that comprise both continuous and categorical inputs is important, yet poses significant challenges. We propose a new approach, Continuous and Categorical Bayesian Optimisation (CoCaBO), which combines the strengths of multi-armed bandits and Bayesian optimisation to select …

New framework converts multi-objective to single-objective optimisation.

Study improves Bayesian optimisation with ensemble transfer learning.

ε-shotgun optimizes expensive function evaluations in parallel.

Bayesian optimisation framework considers only variable orderings to handle ill-conditioned objectives.

BOE reformulates BO as a classifier for scalable batch optimisation.

The notion of expense in Bayesian optimisation generally refers to the uniformly expensive cost of function evaluations over the whole search space. However, in some scenarios, the cost of evaluation for black-box objective functions is non-uniform since different inputs from search space may incur different costs for …

Proposes PE-GP-UCB for time-varying Bayesian optimisation.

Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable. This work shows that such attri…

Bayesian Optimisation (BO) refers to a suite of techniques for global optimisation of expensive black box functions, which use introspective Bayesian models of the function to efficiently search for the optimum. While BO has been applied successfully in many applications, modern optimisation tasks usher in new challeng…

AEGiS optimizes expensive function evaluations asynchronously.

New Bayesian models optimize quantiles and expectiles for stochastic functions.

Bandit methods for black-box optimisation, such as Bayesian optimisation, are used in a variety of applications including hyper-parameter tuning and experiment design. Recently, \emph{multi-fidelity} methods have garnered considerable attention since function evaluations have become increasingly expensive in such appli…

Bayesian optimisation is improved by incorporating expert prior through space warping.

New methods improve global optimisation for expensive functions using lookahead strategies.

Bayesian optimisation outperforms standard ML-II in small samples.

Bayesian optimisation (BO) is a well-known efficient algorithm for finding the global optimum of expensive, black-box functions. The current practical BO algorithms have regret bounds ranging from $\mathcal{O}(\frac{logN}{\sqrt{N}})$ to $\mathcal O(e^{-\sqrt{N}})$, where $N$ is the number of evaluations. This paper exp…

New method optimizes multiple points in Bayesian optimization efficiently.

New method uses random decompositions for high-dimensional Bayesian optimization.

Bayesian optimisation improves with fully-Bayesian treatment of hyperparameters.

Bayesian optimisation's mean function choice affects convergence speed.

Techniques known as Nonlinear Set Membership prediction, Kinky Inference or Lipschitz Interpolation are fast and numerically robust approaches to nonparametric machine learning that have been proposed to be utilised in the context of system identification and learning-based control. They utilise presupposed Lipschitz p…

New algorithms minimize noisy, irregular functions without gradients.

Bayesian optimisation method targets graph classification models against adversarial attacks.

In many contexts the modal properties of a structure change, either due to the impact of a changing environment, fatigue, or due to the presence of structural damage. For example during flight, an aircraft's modal properties are known to change with both altitude and velocity. It is thus important to quantify these cha…

Paper presents efficient IS for tail risk estimation with machine learning features.

EDRBO optimizes Bayesian optimization with continuous contexts using ensemble models and robust methods.

Marich extracts high-fidelity models from public data with minimal queries.

New algorithm optimizes noisy function evaluations with delayed feedback.

MVRSM optimizes expensive functions with mixed variables, outperforming state-of-the-art methods.

In this paper we model the loss function of high-dimensional optimization problems by a Gaussian random field, or equivalently a Gaussian process. Our aim is to study gradient descent in such loss functions or energy landscapes and compare it to results obtained from real high-dimensional optimization problems such as …

BOBYQA optimizes deep networks with fewer queries than other methods.

AntBO optimizes antibody design using Bayesian optimization for efficient and effective CDRH3 sequence generation.

This paper explores optimising acquisition functions in Bayesian optimisation.

We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning. This is accomplished by a probabilistic model-based approach that explains observed data while capturing predictive uncertainty during the d…