arXiv research

New method optimises worst-case risk under model uncertainty.

This work explores robust multi-objective optimisation with scalarisation and robustification.

Paper develops robust SGLD for solving non-convex DRO problems.

In this paper, we formalise order-robust optimisation as an instance of online learning minimising simple regret, and propose Vroom, a zero'th order optimisation algorithm capable of achieving vanishing regret in non-stationary environments, while recovering favorable rates under stochastic reward-generating processes.…

We extend Relative Robust Portfolio Optimisation models to allow portfolios to optimise their distance to a set of benchmarks. Portfolio managers are also given the option of computing regret in a way which is more in line with market practices than other approaches suggested in the literature. In addition, they are gi…

Bayesian optimisation has been successfully applied to a variety of reinforcement learning problems. However, the traditional approach for learning optimal policies in simulators does not utilise the opportunity to improve learning by adjusting certain environment variables: state features that are unobservable and ran…

Bayesian optimisation outperforms standard ML-II in small samples.

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

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…

Bayesian surrogate models reduce uncertainty in high-dimensional design optimisation problems.

Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world. This paper proposes $\text{W}\text{R}^{2}\text{L}$ -- a robust reinforcement learning algorithm with significant robust performance on low and high-dimensional control tasks. Ou…

Investigates portfolio optimization with and without gearing constraints.

Bayesian optimisation is a sample-efficient search methodology that holds great promise for accelerating drug and materials discovery programs. A frequently-overlooked modelling consideration in Bayesian optimisation strategies however, is the representation of heteroscedastic aleatoric uncertainty. In many practical a…

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…

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…

This paper is on Bayesian inference for parametric statistical models that are defined by a stochastic simulator which specifies how data is generated. Exact sampling is then possible but evaluating the likelihood function is typically prohibitively expensive. Approximate Bayesian Computation (ABC) is a framework to pe…

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

Paper introduces robust Gaussian process regression without sacrificing computational efficiency.

Bayesian optimisation method targets graph classification models against adversarial attacks.

Backdoor attacks are found to be effective against robust machine learning models trained with PGD.

Policy gradient methods ignore the potential value of adjusting environment variables: unobservable state features that are randomly determined by the environment in a physical setting, but are controllable in a simulator. This can lead to slow learning, or convergence to suboptimal policies, if the environment variabl…

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

A risk-aware RL approach using RDEU and Wasserstein ball for robust performance.

Regularisation improves ML classifier stability against poisoning attacks.

A new algorithm for deep Q-learning with robustness to state transition uncertainty.

Geometric methods solve sampling, optimisation, inference, and adaptive decision-making.

We introduce a new algorithm for reinforcement learning called Maximum aposteriori Policy Optimisation (MPO) based on coordinate ascent on a relative entropy objective. We show that several existing methods can directly be related to our derivation. We develop two off-policy algorithms and demonstrate that they are com…

Despite the improved accuracy of deep neural networks, the discovery of adversarial examples has raised serious safety concerns. In this paper, we study two variants of pointwise robustness, the maximum safe radius problem, which for a given input sample computes the minimum distance to an adversarial example, and the …

Efficient algorithms compute lambda quantiles for robust portfolio optimization.

Improves DRO with Bayesian Ambiguity Sets for model misspecification.

Mixture modelling using elliptical distributions promises enhanced robustness, flexibility and stability over the widely employed Gaussian mixture model (GMM). However, existing studies based on the elliptical mixture model (EMM) are restricted to several specific types of elliptical probability density functions, whic…

We address the estimation problem for general finite mixture models, with a particular focus on the elliptical mixture models (EMMs). Compared to the widely adopted Kullback-Leibler divergence, we show that the Wasserstein distance provides a more desirable optimisation space. We thus provide a stable solution to the E…

New method calibrates ambiguity sets for robust decision-making under contamination.

Overlay framework simplifies exotic derivative pricing.

Improved portfolio optimization using GAM factor models.

This paper explores optimising acquisition functions in Bayesian optimisation.

Deep Gaussian processes on manifolds improve performance on complex data.

Study uses RL to hedge financial derivatives, showing robust strategies outperform non-robust ones.

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

Unified Bayesian Optimisation for mixed variables improves performance.

We present a probabilistic viewpoint to multiple kernel learning unifying well-known regularised risk approaches and recent advances in approximate Bayesian inference relaxations. The framework proposes a general objective function suitable for regression, robust regression and classification that is lower bound of the…

Develops a neural surrogate for proton dose calculation using Monte Carlo dropout uncertainty.

New algorithm robust to outliers in Bayesian Optimization.

Paper uses variational inference to estimate nonlinear models.

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

Develops a two-layer model to design mortgage assistance products.

Neural FGP learns portfolio generating functions from data.

A cost-effective approach to label acquisition using active learning markets.