arXiv research

New method optimizes Gaussian process allocation for BO.

New algorithm finds sparse matrices on Stiefel manifold for optimisation.

This study uses continuous-time analysis to understand how momentum affects the optimisation of diagonal linear networks.

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

New method trains sparse Gaussian processes without matrix inversion.

We introduce a means of automating machine learning (ML) for big data tasks, by performing scalable stochastic Bayesian optimisation of ML algorithm parameters and hyper-parameters. More often than not, the critical tuning of ML algorithm parameters has relied on domain expertise from experts, along with laborious hand…

In-BO optimizes complex constrained domains using SIn-GP surrogate models.

Unified approach to Bayesian inference with guarantees on covariance matrices.

Bayesian methods improve drug discovery experiment design.

Sparse GEMINI selects relevant features for clustering without assumptions.

DeepMP improves non-negative sparse recovery performance.

Sparse Markovian Gaussian processes improve probabilistic model inference for large datasets.

We introduce a novel paradigm for learning non-parametric drift and diffusion functions for stochastic differential equation (SDE). The proposed model learns to simulate path distributions that match observations with non-uniform time increments and arbitrary sparseness, which is in contrast with gradient matching that…

A new algorithm solves sparse optimization problems on measures efficiently.

Paper introduces robust Gaussian process regression without sacrificing computational efficiency.

The Bayesian Optimisation Algorithm (BOA) is an Estimation of Distribution Algorithm (EDA) that uses a Bayesian network as probabilistic graphical model (PGM). Determining the optimal Bayesian network structure given a solution sample is an NP-hard problem. This step should be completed at each iteration of BOA, result…

Various $\ell_1$-penalised estimation methods such as graphical lasso and CLIME are widely used for sparse precision matrix estimation. Many of these methods have been shown to be consistent under various quantitative assumptions about the underlying true covariance matrix. Intuitively, these conditions are related to …

DistGP models multi-robot mapping with distributed Gaussian process learning.

We present a new method for forecasting systems of multiple interrelated time series. The method learns the forecast models together with discovering leading indicators from within the system that serve as good predictors improving the forecast accuracy and a cluster structure of the predictive tasks around these. The …

Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support deployment of GPs in the large data regime and enable analytic intractabilities to be sidestepped. However, the field lacks a principled method to handle streaming data in which both the posterior distribution ov…

We address the problem of partial index tracking, replicating a benchmark index using a small number of assets. Accurate tracking with a sparse portfolio is extensively studied as a classic finance problem. However in practice, a tracking portfolio must also be diverse in order to minimise risk -- a requirement which h…

This paper explores optimising acquisition functions in Bayesian optimisation.

Gaussian Processes improve data interpolation from diverse experiments.

We propose a method for recovering the structure of a sparse undirected graphical model when very few samples are available. The method decides about the presence or absence of bonds between pairs of variable by considering one pair at a time and using a closed form formula, analytically derived by calculating the post…

WamOL uses PINNs to efficiently calibrate IVS from sparse data.

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.

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

Early alignment in neural networks leads to sparse representations but hinders convergence.

O3 optimizes samples from generative models without extra training.

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

Bayesian optimisation framework for graph functions.

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.

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 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…

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 …