28 citations · 130 across the 23 of their papers we have counts for
5 papers · 1 filter
BOSS: Bayesian Optimization over String Spaces
Henry B. Moss, Daniel Beck, Javier Gonzalez +2
This article develops a Bayesian optimization (BO) method which acts directly over raw strings, proposing the first uses of string kernels and genetic algorithms within BO loops. R…
Learning to Rank under Multinomial Logit Choice
James A. Grant, David S. Leslie
Learning the optimal ordering of content is an important challenge in website design. The learning to rank (LTR) framework models this problem as a sequential problem of selecting…
BOSH: Bayesian Optimization by Sampling Hierarchically
Henry B. Moss, David S. Leslie, Paul Rayson
Deployments of Bayesian Optimization (BO) for functions with stochastic evaluations, such as parameter tuning via cross validation and simulation optimization, typically optimize a…
MUMBO: MUlti-task Max-value Bayesian Optimization
Henry B. Moss, David S. Leslie, Paul Rayson
We propose MUMBO, the first high-performing yet computationally efficient acquisition function for multi-task Bayesian optimization. Here, the challenge is to perform efficient opt…
On Thompson Sampling for Smoother-than-Lipschitz Bandits
James A. Grant, David S. Leslie
Thompson Sampling is a well established approach to bandit and reinforcement learning problems. However its use in continuum armed bandit problems has received relatively little at…