2 citations · 6 across the 14 of their papers we have counts for
5 papers · 1 filter
Just One Layer Norm Guarantees Stable Extrapolation
Juliusz Ziomek, George Whittle, Michael A. Osborne
In spite of their prevalence, the behaviour of Neural Networks when extrapolating far from the training distribution remains poorly understood, with existing results limited to spe…
Mean-Field Bayesian Optimisation
Petar Steinberg, Juliusz Ziomek, Matej Jusup +1
We address the problem of optimising the average payoff for a large number of cooperating agents, where the payoff function is unknown and treated as a black box. While standard Ba…
Time-Varying Gaussian Process Bandits with Unknown Prior
Juliusz Ziomek, Masaki Adachi, Michael A. Osborne
Bayesian optimisation requires fitting a Gaussian process model, which in turn requires specifying prior on the unknown black-box function -- most of the theoretical literature ass…
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?
Juliusz Ziomek, Haitham Bou-Ammar
Learning decompositions of expensive-to-evaluate black-box functions promises to scale Bayesian optimisation (BO) to high-dimensional problems. However, the success of these techni…
Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints
David Mguni, Aivar Sootla, Juliusz Ziomek +4
Many real-world settings involve costs for performing actions; transaction costs in financial systems and fuel costs being common examples. In these settings, performing actions at…