In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Moriconi:2020:10.1007/s10994-020-05899-z,
author = {Moriconi, R and Deisenroth, M and Karri, S},
doi = {10.1007/s10994-020-05899-z},
journal = {Machine Learning},
pages = {1925--1943},
title = {High-dimensional Bayesian optimization usinglow-dimensional feature spaces},
url = {http://dx.doi.org/10.1007/s10994-020-05899-z},
volume = {109},
year = {2020}
}
TY - JOUR
AB - Bayesian optimization (BO) is a powerful approach for seeking the global optimum of expensive black-box functions and has proven successful for fine tuning hyper-parameters of machine learning models. However, BO is practically limited to optimizing 10–20 parameters. To scale BO to high dimensions, we usually make structural assumptions on the decomposition of the objective and/or exploit the intrinsic lower dimensionality of the problem, e.g. by using linear projections. We could achieve a higher compression rate with nonlinear projections, but learning these nonlinear embeddings typically requires much data. This contradicts the BO objective of a relatively small evaluation budget. To address this challenge, we propose to learn a low-dimensional feature space jointly with (a) the response surface and (b) a reconstruction mapping. Our approach allows for optimization of BO’s acquisition function in the lower-dimensional subspace, which significantly simplifies the optimization problem. We reconstruct the original parameter space from the lower-dimensional subspace for evaluating the black-box function. For meaningful exploration, we solve a constrained optimization problem.
AU - Moriconi,R
AU - Deisenroth,M
AU - Karri,S
DO - 10.1007/s10994-020-05899-z
EP - 1943
PY - 2020///
SN - 0885-6125
SP - 1925
TI - High-dimensional Bayesian optimization usinglow-dimensional feature spaces
T2 - Machine Learning
UR - http://dx.doi.org/10.1007/s10994-020-05899-z
UR - https://link.springer.com/article/10.1007%2Fs10994-020-05899-z
UR - http://hdl.handle.net/10044/1/82883
VL - 109
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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