Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning. - Matt Benatan; Edward O. Pyzer-Knapp (CoRR, 2019)

Modelling Mobile Signal Strength by Combining Geospatial Big Data and Artificial Intelligence. - Paolo Fraccaro; Matt Benatan; Katharina Reusch; Clyde Fare; Blair Edwards; Edward Pyzer-Knapp (ICVISP, 2020)

Two-stage Classification for Detecting Murmurs from Phonocardiograms Using Deep and Expert Features. - Sara Summerton; Danny Wood; Darcy Murphy; Oliver C. Redfern; Matt Benatan; Matti Kaisti; David C. Wong 004 (CinC, 2022)

SonOpt: Sonifying Bi-objective Population-Based Optimization Algorithms. - Tasos Asonitis; Richard Allmendinger 001; Matt Benatan; Ricardo Climent (EvoMUSART, 2022)

Expensive optimization with production-graph resource constraints: a first look at a new problem class. - Stefan Pricopie; Richard Allmendinger 001; Manuel López-Ibáñez 001; Clyde Fare; Matt Benatan; Joshua D. Knowles (GECCO, 2022)

SonOpt: understanding the behaviour of bi-objective population-based optimisation algorithms through sound. - Tasos Asonitis; Richard Allmendinger 001; Matt Benatan; Ricardo Climent (Genet. Program. Evolvable Mach., 2023)

Bayesian Optimization with Setup Switching Cost. - Stefan Pricopie; Richard Allmendinger 001; Manuel López-Ibáñez 001; Clyde Fare; Matt Benatan; Joshua D. Knowles (GECCO Companion, 2024)

An Adaptive Approach to Bayesian Optimization with Setup Switching Costs. - Stefan Pricopie; Richard Allmendinger 001; Manuel López-Ibáñez 001; Clyde Fare; Matt Benatan; Joshua D. Knowles (PPSN (2), 2024)

An adaptive approach to Bayesian Optimization with switching costs. - Stefan Pricopie; Richard Allmendinger 001; Manuel López-Ibáñez 001; Clyde Fare; Matt Benatan; Joshua D. Knowles (CoRR, 2024)

Model-agnostic variable importance for predictive uncertainty: an entropy-based approach. - Danny Wood; Theodore Papamarkou; Matt Benatan; Richard Allmendinger 001 (Data Min. Knowl. Discov., 2024)