621 research outputs found
BOSH:Bayesian Optimization by Sampling Hierarchically
Deployments of Bayesian Optimization (BO) for functions with stochastic evaluations, such as parameter tuning via cross validation and simulation optimization, typically optimize an average of a fixed set of noisy realizations of the objective function. However, disregarding the true objective function in this manner finds a high-precision optimum of the wrong function. To solve this problem, we propose Bayesian Optimization by Sampling Hierarchically (BOSH), a novel BO routine pairing a hierarchical Gaussian process with an information-theoretic framework to generate a growing pool of realizations as the optimization progresses. We demonstrate that BOSH provides more efficient and higher-precision optimization than standard BO across synthetic benchmarks, simulation optimization, reinforcement learning and hyper-parameter tuning tasks
BOSS: Bayesian Optimization over String Spaces
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. Recent applications of BO over strings have been
hindered by the need to map inputs into a smooth and unconstrained latent
space. Learning this projection is computationally and data-intensive. Our
approach instead builds a powerful Gaussian process surrogate model based on
string kernels, naturally supporting variable length inputs, and performs
efficient acquisition function maximization for spaces with syntactical
constraints. Experiments demonstrate considerably improved optimization over
existing approaches across a broad range of constraints, including the popular
setting where syntax is governed by a context-free grammar
MUMBO:MUlti-task Max-value Bayesian Optimization
We propose MUMBO, the first high-performing yet computationally efficient acquisition function for multi-task Bayesian optimization. Here, the challenge is to perform efficient optimization by evaluating low-cost functions somehow related to our true target function. This is a broad class of problems including the popular task of multi-fidelity optimization. However, while information-theoretic acquisition functions are known to provide state-of-the-art Bayesian optimization, existing implementations for multi-task scenarios have prohibitive computational requirements. Previous acquisition functions have therefore been suitable only for problems with both low-dimensional parameter spaces and function query costs sufficiently large to overshadow very significant optimization overheads. In this work, we derive a novel multi-task version of entropy search, delivering robust performance with low computational overheads across classic optimization challenges and multi-task hyper-parameter tuning. MUMBO is scalable and efficient, allowing multi-task Bayesian optimization to be deployed in problems with rich parameter and fidelity spaces
GIBBON: General-purpose Information-Based Bayesian Optimisation
This paper describes a general-purpose extension of max-value entropy search, a popular approach for Bayesian Optimisation (BO). A novel approximation is proposed for the information gain -- an information-theoretic quantity central to solving a range of BO problems, including noisy, multi-fidelity and batch optimisations across both continuous and highly-structured discrete spaces. Previously, these problems have been tackled separately within information-theoretic BO, each requiring a different sophisticated approximation scheme, except for batch BO, for which no computationally-lightweight information-theoretic approach has previously been proposed. GIBBON (General-purpose Information-Based Bayesian OptimisatioN) provides a single principled framework suitable for all the above, out-performing existing approaches whilst incurring substantially lower computational overheads. In addition, GIBBON does not require the problem's search space to be Euclidean and so is the first high-performance yet computationally light-weight acquisition function that supports batch BO over general highly structured input spaces like molecular search and gene design. Moreover, our principled derivation of GIBBON yields a natural interpretation of a popular batch BO heuristic based on determinantal point processes. Finally, we analyse GIBBON across a suite of synthetic benchmark tasks, a molecular search loop, and as part of a challenging batch multi-fidelity framework for problems with controllable experimental noise
FIESTA:Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms
We present FIESTA, a model selection approach that significantly reduces the computational resources required to reliably identify state-of-the-art performance from large collections of candidate models. Despite being known to produce unreliable comparisons, it is still common practice to compare model evaluations based on single choices of random seeds. We show that reliable model selection also requires evaluations based on multiple train-test splits (contrary to common practice in many shared tasks). Using bandit theory from the statistics literature, we are able to adaptively determine appropriate numbers of data splits and random seeds used to evaluate each model, focusing computational resources on the evaluation of promising models whilst avoiding wasting evaluations on models with lower performance. Furthermore, our user-friendly Python implementation produces confidence guarantees of correctly selecting the optimal model. We evaluate our algorithms by selecting between 8 target-dependent sentiment analysis methods using dramatically fewer model evaluations than current model selection approaches
Data-driven discovery of molecular photoswitches with multioutput Gaussian processes
Photoswitchable molecules display two or more isomeric forms that may be accessed using light. Separating the electronic absorption bands of these isomers is key to selectively addressing a specific isomer and achieving high photostationary states whilst overall red-shifting the absorption bands serves to limit material damage due to UV-exposure and increases penetration depth in photopharmacological applications. Engineering these properties into a system through synthetic design however, remains a challenge. Here, we present a data-driven discovery pipeline for molecular photoswitches underpinned by dataset curation and multitask learning with Gaussian processes. In the prediction of electronic transition wavelengths, we demonstrate that a multioutput Gaussian process (MOGP) trained using labels from four photoswitch transition wavelengths yields the strongest predictive performance relative to single-task models as well as operationally outperforming time-dependent density functional theory (TD-DFT) in terms of the wall-clock time for prediction. We validate our proposed approach experimentally by screening a library of commercially available photoswitchable molecules. Through this screen, we identified several motifs that displayed separated electronic absorption bands of their isomers, exhibited red-shifted absorptions, and are suited for information transfer and photopharmacological applications. Our curated dataset, code, as well as all models are made available at https://github.com/Ryan-Rhys/The-Photoswitch-Dataset
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
We present Trieste, an open-source Python package for Bayesian optimization
and active learning benefiting from the scalability and efficiency of
TensorFlow. Our library enables the plug-and-play of popular TensorFlow-based
models within sequential decision-making loops, e.g. Gaussian processes from
GPflow or GPflux, or neural networks from Keras. This modular mindset is
central to the package and extends to our acquisition functions and the
internal dynamics of the decision-making loop, both of which can be tailored
and extended by researchers or engineers when tackling custom use cases.
Trieste is a research-friendly and production-ready toolkit backed by a
comprehensive test suite, extensive documentation, and available at
https://github.com/secondmind-labs/trieste
Single hadron response measurement and calorimeter jet energy scale uncertainty with the ATLAS detector at the LHC
The uncertainty on the calorimeter energy response to jets of particles is
derived for the ATLAS experiment at the Large Hadron Collider (LHC). First, the
calorimeter response to single isolated charged hadrons is measured and
compared to the Monte Carlo simulation using proton-proton collisions at
centre-of-mass energies of sqrt(s) = 900 GeV and 7 TeV collected during 2009
and 2010. Then, using the decay of K_s and Lambda particles, the calorimeter
response to specific types of particles (positively and negatively charged
pions, protons, and anti-protons) is measured and compared to the Monte Carlo
predictions. Finally, the jet energy scale uncertainty is determined by
propagating the response uncertainty for single charged and neutral particles
to jets. The response uncertainty is 2-5% for central isolated hadrons and 1-3%
for the final calorimeter jet energy scale.Comment: 24 pages plus author list (36 pages total), 23 figures, 1 table,
submitted to European Physical Journal
Standalone vertex ďŹnding in the ATLAS muon spectrometer
A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths. The performance of the algorithm is evaluated using both a sample of simulated Higgs boson events, in which the Higgs boson decays to long-lived neutral particles that in turn decay to bbar b final states, and pp collision data at âs = 7 TeV collected with the ATLAS detector at the LHC during 2011
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