Search Results for author:
Found 7 papers, 5 papers with code
PRoA: A Probabilistic Robustness Assessment against Functional Perturbations
Our experiments demonstrate the effectiveness and flexibility of PRoA in terms of evaluating the probabilistic robustness against a broad range of functional perturbations, and PRoA can scale well to various large-scale deep neural networks compared to existing state-of-the-art baselines.
Variational Autoencoders Without the Variation
Recent work on regularised and entropic autoencoders have begun to explore the potential, for generative modelling, of removing the variational approach and returning to the classic deterministic autoencoder (DAE) with additional novel regularisation methods.
Asynchronous ε-Greedy Bayesian Optimisation
Batch Bayesian optimisation (BO) is a successful technique for the optimisation of expensive black-box functions.
What do you Mean? The Role of the Mean Function in Bayesian Optimisation
We show that the rate of convergence can depend sensitively on the choice of mean function.
$ε$-shotgun: $ε$-greedy Batch Bayesian Optimisation
Bayesian optimisation is a popular, surrogate model-based approach for optimising expensive black-box functions.
Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation
The performance of acquisition functions for Bayesian optimisation to locate the global optimum of continuous functions is investigated in terms of the Pareto front between exploration and exploitation.
Bayesian Search for Robust Optima
We propose a method for robust optimisation using Bayesian optimisation to find a region of design space in which the expensive function's performance is relatively insensitive to the inputs whilst retaining a good quality.
