arXiv research

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

Bayesian optimisation framework for graph functions.

A novel optimisation framework through quadratic nonlinear projection is introduced for credit portfolio when the portfolio risk is measured by Conditional Value-at-Risk (CVaR). The whole optimisation procedure to search toward the optimal portfolio state is conducted by a series of single-step optimisations under the …

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

Batch Bayesian optimisation (BO) has been successfully applied to hyperparameter tuning using parallel computing, but it is wasteful of resources: workers that complete jobs ahead of others are left idle. We address this problem by developing an approach, Penalising Locally for Asynchronous Bayesian Optimisation on $k$…

We present an efficient block-diagonal ap- proximation to the Gauss-Newton matrix for feedforward neural networks. Our result- ing algorithm is competitive against state- of-the-art first order optimisation methods, with sometimes significant improvement in optimisation performance. Unlike first-order methods, for whic…

FP uses random projections to train networks without feedback, achieving comparable performance to backpropagation.

We consider the problem of inverse kinematics (IK), where one wants to find the parameters of a given kinematic skeleton that best explain a set of observed 3D joint locations. The kinematic skeleton has a tree structure, where each node is a joint that has an associated geometric transformation that is propagated to a…

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…

Optimizes one-class classification methods for better performance.

New method optimizes Gaussian process allocation for BO.

Heuristic optimisers which search for an optimal configuration of variables relative to an objective function often get stuck in local optima where the algorithm is unable to find further improvement. The standard approach to circumvent this problem involves periodically restarting the algorithm from random initial con…

In this paper, we study a semi-martingale optimal transport problem and its application to the calibration of Local-Stochastic Volatility (LSV) models. Rather than considering the classical constraints on marginal distributions at initial and final time, we optimise our cost function given the prices of a finite number…

Paper shows Cox model optimisation leaks patient data in distributed learning.

Stochastic gradient descent optimizes Nyström samples for kernel matrix approximation.

Deep Neural Network (DNN) acoustic models often use discriminative sequence training that optimises an objective function that better approximates the word error rate (WER) than frame-based training. Sequence training is normally implemented using Stochastic Gradient Descent (SGD) or Hessian Free (HF) training. This pa…

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…

A recent novel extension of multi-output Gaussian processes handles heterogeneous outputs assuming that each output has its own likelihood function. It uses a vector-valued Gaussian process prior to jointly model all likelihoods' parameters as latent functions drawn from a Gaussian process with a linear model of coregi…

This paper explores optimising acquisition functions in Bayesian optimisation.

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

Unified Bayesian Optimisation for mixed variables improves performance.

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

Study improves Bayesian optimisation with ensemble transfer learning.

AdamZ optimiser improves neural network training efficiency.

Wind farm layout optimisation tackles space constraints with Bayesian multi-objective approach.

Book covers tools for zeroth-order convex optimisation.

O3 optimizes samples from generative models without extra training.

A new optimisation method efficiently scales Hessian-vector products for neural networks.

SLT reveals how grokking occurs via basin selection in training.

Bayesian optimisation tackles expensive black-box functions with constraints.

MTL2L learns to adapt optimisation rules for unseen data.

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…

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…

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

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

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…

Deep Optimisation (DO) combines evolutionary search with Deep Neural Networks (DNNs) in a novel way - not for optimising a learning algorithm, but for finding a solution to an optimisation problem. Deep learning has been successfully applied to classification, regression, decision and generative tasks and in this paper…

One-pass optimisation for high-dimensional hyperparameters.

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

We introduce the Locally Linear Latent Variable Model (LL-LVM), a probabilistic model for non-linear manifold discovery that describes a joint distribution over observations, their manifold coordinates and locally linear maps conditioned on a set of neighbourhood relationships. The model allows straightforward variatio…

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 approach separates VAE and GP for better molecular optimisation.

Optimizes data power control in cell-free networks for better spectral efficiency.

Novel algorithms for entropic optimal transport from an optimisation perspective.

Study analyzes a new algorithm for complex optimization problems.

New adaptive importance samplers improve stability and accuracy.

A simplified model for fixed income portfolio optimisation.