High-dimensional Bayesian optimization with projections using quantile Gaussian processes

Moriconi, Riccardo; Kumar, Krishna; Deisenroth, Marc Peter

doi:10.1007/s11590-019-01433-w

Cited by 24 publications

(14 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our code is made publicly available at https://github.com/Ryan-Rhys/ Constrained-Bayesian-Optimisation-for-Automatic-Chemical-Design. Further work could feature improvements to the constraint scheme Rainforth et al [2016], Mahmood and Hernández-Lobato [2019], Astudillo and Frazier [2019], 201, Moriconi et al [2019], Bartz-Beielstein and Zaefferer [2017]. In terms of objectives for molecule generation, recent work by , Polykovskiy et al [2018a], Tabor et al [2018], Aumentado-Armstrong [2018], has featured a more targeted search for novel compounds.…”

Section: Discussionmentioning

confidence: 99%

Constrained Bayesian optimization for automatic chemical design using variational autoencoders

2020

View full text Add to dashboard Cite

Automatic Chemical Design is a framework for generating novel molecules with optimized properties. The original scheme, featuring Bayesian optimization over the latent space of a variational autoencoder, suffers from the pathology that it tends to produce invalid molecular structures. First, we demonstrate empirically that this pathology arises when the Bayesian optimization scheme queries latent points far away from the data on which the variational autoencoder has been trained. Secondly, by reformulating the search procedure as a constrained Bayesian optimization problem, we show that the effects of this pathology can be mitigated, yielding marked improvements in the validity of the generated molecules. We posit that constrained Bayesian optimization is a good approach for solving this class of training set mismatch in many generative tasks involving Bayesian optimization over the latent space of a variational autoencoder.

show abstract

Section: Discussionmentioning

confidence: 99%

Constrained Bayesian optimization for automatic chemical design using variational autoencoders

2020

View full text Add to dashboard Cite

show abstract

“…High-dimensional optimization is often translated into low-dimensional problems, which are defined on subsets of variables (Moriconi et al 2020;Kandasamy et al 2015;Rolland et al 2018). These approaches apply a divide and conquer approach to decompose the problem into independent (Moriconi et al 2020;Kandasamy et al 2015) and potentially dependent components (Rolland et al 2018). However, high-dimensional data often possesses a lower intrinsic dimensionality, which can be exploited for optimization.…”

Section: Introductionmentioning

confidence: 99%

High-dimensional Bayesian optimization using low-dimensional feature spaces

2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…where f ( x best ) is the lowest observed value, Φ and ϕ are the standard cumulative and normal density, respectively [ 62 ].…”

Section: Resultsmentioning

confidence: 99%

Succinylation Site Prediction Based on Protein Sequences Using the IFS-LightGBM (BO) Model

Zhang

Liu

Qin

et al. 2020

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Succinylation is an important posttranslational modification of proteins, which plays a key role in protein conformation regulation and cellular function control. Many studies have shown that succinylation modification on protein lysine residue is closely related to the occurrence of many diseases. To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately. In this study, we develop a new model, IFS-LightGBM (BO), which utilizes the incremental feature selection (IFS) method, the LightGBM feature selection method, the Bayesian optimization algorithm, and the LightGBM classifier, to predict succinylation sites in proteins. Specifically, pseudo amino acid composition (PseAAC), position-specific scoring matrix (PSSM), disorder status, and Composition of k -spaced Amino Acid Pairs (CKSAAP) are firstly employed to extract feature information. Then, utilizing the combination of the LightGBM feature selection method and the incremental feature selection (IFS) method selects the optimal feature subset for the LightGBM classifier. Finally, to increase prediction accuracy and reduce the computation load, the Bayesian optimization algorithm is used to optimize the parameters of the LightGBM classifier. The results reveal that the IFS-LightGBM (BO)-based prediction model performs better when it is evaluated by some common metrics, such as accuracy, recall, precision, Matthews Correlation Coefficient (MCC), and F -measure.

show abstract

High-dimensional Bayesian optimization with projections using quantile Gaussian processes

Cited by 24 publications

References 10 publications

Constrained Bayesian optimization for automatic chemical design using variational autoencoders

Constrained Bayesian optimization for automatic chemical design using variational autoencoders

High-dimensional Bayesian optimization using low-dimensional feature spaces

Succinylation Site Prediction Based on Protein Sequences Using the IFS-LightGBM (BO) Model

Contact Info

Product

Resources

About