arXiv research

AVATAR uses a surrogate model to quickly evaluate ML pipelines, saving time and resources.

Study improves Bayesian optimisation with ensemble transfer learning.

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…

Enhances hedging strategies using deep neural networks.

We introduce a flexible, scalable Bayesian inference framework for nonlinear dynamical systems characterised by distinct and hierarchical variability at the individual, group, and population levels. Our model class is a generalisation of nonlinear mixed-effects (NLME) dynamical systems, the statistical workhorse for ma…

This work improves AutoML systems by dynamically evaluating fitness to reduce overfitting.

Fair Adversarial Networks remove bias from data.

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.

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.

O3 optimizes samples from generative models without extra training.

Hyperparameter tuning of multi-stage pipelines introduces a significant computational burden. Motivated by the observation that work can be reused across pipelines if the intermediate computations are the same, we propose a pipeline-aware approach to hyperparameter tuning. Our approach optimizes both the design and exe…

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…

Classical Machine Learning (ML) pipelines often comprise of multiple ML models where models, within a pipeline, are trained in isolation. Conversely, when training neural network models, layers composing the neural models are simultaneously trained using backpropagation. We argue that the isolated training scheme of ML…

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

Study proposes a statistical testing framework for evaluating clustering pipelines.

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…

Investigates fairness in pipeline models where individuals may drop out.

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 …

New approach separates VAE and GP for better molecular optimisation.

Data science relies on pipelines that are organized in the form of interdependent computational steps. Each step consists of various candidate algorithms that maybe used for performing a particular function. Each algorithm consists of several hyperparameters. Algorithms and hyperparameters must be optimized as a whole …

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.

Automates supervised learning pipeline design with matrix and tensor factorization.

New adaptive importance samplers improve stability and accuracy.

Data science relies on pipelines that are organized in the form of interdependent computational steps. Each step consists of various candidate algorithms that maybe used for performing a particular function. Each algorithm consists of several hyperparameters. Algorithms and hyperparameters must be optimized as a whole …

A simplified model for fixed income portfolio optimisation.

New model optimizes oil product distribution via pipelines.

Pipeline decomposes portfolio optimization problems into smaller, solvable subproblems.

Paper proposes a statistical test for feature selection pipelines using selective inference.

TomOpt optimizes muon detector designs using differentiable programming.

Improved regret bound for adversarial bandit convex optimisation.