12 citations · 14 across the 5 of their papers we have counts for
5 papers
Ordinal Optimisation and the Offline Multiple Noisy Secretary Problem
Robert Chin, Jonathan E. Rowe, Iman Shames +2
We study the success probability for a variant of the secretary problem, with noisy observations and multiple offline selection. Our formulation emulates, and is motivated by, prob…
A Sequential Learning Algorithm for Probabilistically Robust Controller Tuning
Robert Chin, Chris Manzie, Iman Shames +2
We introduce a sequential learning algorithm to address a robust controller tuning problem, which in effect, finds (with high probability) a candidate solution satisfying the inter…
Active Learning for Linear Parameter-Varying System Identification
Robert Chin, Alejandro I. Maass, Nalika Ulapane +5
Active learning is proposed for selection of the next operating points in the design of experiments, for identifying linear parameter-varying systems. We extend existing approaches…
Tuning of multivariable model predictive controllersthrough expert bandit feedback
Alex. S. Ira, Chris Manzie, Iman Shames +4
For certain industrial control applications an explicit function capturing the nontrivial trade-off between competing objectives in closed loop performance is not available. In suc…
Ordinal Optimisation for the Gaussian Copula Model
Robert Chin, Jonathan E. Rowe, Iman Shames +2
We present results on the estimation and evaluation of success probabilities for ordinal optimisation over uncountable sets (such as subsets of ). Our formulation i…