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Poster
Multi-objective Bayesian optimisation with preferences over objectives
Majid Abdolshah · Alistair Shilton · Santu Rana · Sunil Gupta · Svetha Venkatesh

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #32
in Algorithms -- AutoML »

We present a multi-objective Bayesian optimisation algorithm that allows the user to express preference-order constraints on the objectives of the type objective A is more important than objective B. These preferences are defined based on the stability of the obtained solutions with respect to preferred objective functions. Rather than attempting to find a representative subset of the complete Pareto front, our algorithm selects those Pareto-optimal points that satisfy these constraints. We formulate a new acquisition function based on expected improvement in dominated hypervolume (EHI) to ensure that the subset of Pareto front satisfying the constraints is thoroughly explored. The hypervolume calculation is weighted by the probability of a point satisfying the constraints from a gradient Gaussian Process model. We demonstrate our algorithm on both synthetic and real-world problems.

Keywords: Optimization · Deep Learning -> Optimization for Deep Networks; Optimization · Non-Convex Optimization; Reinforcement Learning and Planning
[ Paper]  [ Poster] 

Author Information

Majid Abdolshah (Deakin University)
Alistair Shilton (Deakin University)
Santu Rana (Deakin University)
Sunil Gupta (Deakin University)
Svetha Venkatesh (Deakin University)

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