arXiv research

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

SOBER optimizes and quadrates efficiently in parallel for diverse tasks.

Information-theoretic Bayesian optimisation techniques have demonstrated state-of-the-art performance in tackling important global optimisation problems. However, current information-theoretic approaches require many approximations in implementation, introduce often-prohibitive computational overhead and limit the choi…

Despite recent innovations in network architectures and loss functions, training RNNs to learn long-term dependencies remains difficult due to challenges with gradient-based optimisation methods. Inspired by the success of Deep Neuroevolution in reinforcement learning (Such et al. 2017), we explore the use of gradient-…

Myopic investors make suboptimal choices that benefit others, leading to market inefficiencies.

Convex optimisation solves inverse kinematics problems more reliably.

This paper explores optimising acquisition functions in Bayesian optimisation.

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…

Optimising discrete data for a desired characteristic using gradient-based methods involves projecting the data into a continuous latent space and carrying out optimisation in this space. Carrying out global optimisation is difficult as optimisers are likely to follow gradients into regions of the latent space that the…

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

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

Improves Bayesian optimisation for engineering design problems with many variables.

We present GLASSES: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search. The majority of global optimisation approaches in use are myopic, in only considering the impact of the next function value; the non-myopic approaches that do exist are able to consider only a handful of futu…

Study improves Bayesian optimisation with ensemble transfer learning.

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…

New algorithm finds sparse matrices on Stiefel manifold for optimisation.

New method optimizes Gaussian process allocation for BO.

A new method selects inducing points to optimize high-throughput Bayesian optimisation.

Extends hyperparameter transfer across model sizes and modules, improving training speed.

AEGiS optimizes expensive function evaluations asynchronously.

GACBO optimizes unknown causal graphs with interventions.

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…

Automated framework optimizes DNN deployment on Arm CPUs.

New method optimizes multiple points in Bayesian optimization efficiently.

We describe a novel algorithm for noisy global optimisation and continuum-armed bandits, with good convergence properties over any continuous reward function having finitely many polynomial maxima. Over such functions, our algorithm achieves square-root regret in bandits, and inverse-square-root error in optimisation, …

SA-FDR uses simulated annealing for feature selection in high-dimensional data.

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…

Physical systems are modelled and investigated within simulation software in an increasing range of applications. In reality an investigation of the system is often performed by empirical test scenarios which are related to typical situations. Our aim is to derive a method which generates diverse test scenarios each re…

The typical multi-task learning methods for spatio-temporal data prediction involve low-rank tensor computation. However, such a method have relatively weak performance when the task number is small, and we cannot integrate it into non-linear models. In this paper, we propose a two-step suboptimal unitary method (SUM) …

Efficient algorithms compute lambda quantiles for robust portfolio optimization.

Bayesian optimization adapted for experiments with changing environmental conditions.

Wide neural networks with asymmetrical node scaling converge globally and learn features.

New greedy algorithms improve Bayesian optimisation performance.

We provide a simple and efficient algorithm for adversarial $k$-action $d$-outcome non-degenerate locally observable partial monitoring game for which the $n$-round minimax regret is bounded by $6(d+1) k^{3/2} \sqrt{n \log(k)}$, matching the best known information-theoretic upper bound. The same algorithm also achieves…

We present a new model for prediction markets, in which we use risk measures to model agents and introduce a market maker to describe the trading process. This specific choice on modelling tools brings us mathematical convenience. The analysis shows that the whole market effectively approaches a global objective, despi…

A new scheme reduces global search cost by a square root factor.

The study of neurocognitive tasks requiring accurate localisation of activity often rely on functional Magnetic Resonance Imaging, a widely adopted technique that makes use of a pipeline of data processing modules, each involving a variety of parameters. These parameters are frequently set according to the local goal o…

Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothing. These pipelines are commonly suboptimal, given the local optimisation strategy they use, treating each step in isolation. With the advent…

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

Paper tackles federated learning with personalised bandit algorithms.

Develops a new algorithm for estimating model parameters using interacting particle systems.

Unified Bayesian Optimisation for mixed variables improves performance.

Bayesian Optimisation (BO) refers to a class of methods for global optimisation of a function $f$ which is only accessible via point evaluations. It is typically used in settings where $f$ is expensive to evaluate. A common use case for BO in machine learning is model selection, where it is not possible to analytically…

A new method learns priors for Bayesian optimisation to improve performance.

In this study, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of reinforcement learning (RL) in competition tasks. Within the actor-critic RL, we introduce multiple cooperative critics from two levels of the hierarchy and propose a reinforcement learnin…

Paper classifies economic states and optimizes portfolios for stagflationary environments.

MTFL improves UA and speeds convergence in personalised DNNs on edge devices.