On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning

By Matthew D. Hoffman, Bobak Shahriari, Nando de Freitas. 2014

134 citations recorded by the source corpus.

Subfields: Optimization & Training, Neural Architecture Search & AutoML

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Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “Even though multi-armed bandit learning can also be applied to continuous optimization (Munos, 2014), in the context of AutoML it is only used in a finite setting in combination with other optimization techniques (Hoffman et al., 2014; Efimova et al., 2017; Gustafson, 2018; das Dôres et al., 2018).”
Survey on Automated Machine Learning (2019, methodology): “This makes bandit learning especially useful for selecting values for categorical hyperparameters, e.g selecting an ML algorithm (Hoffman et al., 2014; Efimova et al., 2017; Gustafson, 2018; das Dôres et al., 2018).”
Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, background): “A natural choice for the prior distribution is a product of K beta distributions: as this is the conjugate prior to the Bernoulli likelihood, and it leads to efﬁcient posterior updating.”
Learning to Learn for Global Optimization of Black Box Functions (2016, methodology): “We are therefore considering the ﬁnite horizon setting that is popular in AB tests Kohavi et al. [2009], Scott [2010] and is often studied under the umbrella of best arm identiﬁcation in the bandits literature [Bubeck et al., 2009, Gabillon et al., 2011, 2012, Hoffman et al., 2014].”
FLASH: Fast Bayesian Optimization for Data Analytic Pipelines (2016, background): “Various forms of acquisition functions have been proposed [43, 20, 45, 21].”
Automatic tuning of hyperparameters using Bayesian optimization (2020, methodology): “…and monitoring (Marchant and Ramos 2012), algorithm configuration using optimization (Hutter et al. 2014), automatic machine learning (Snoek et al. 2012; Bergstra et al. 2011; Hoffman et al. 2014), reinforcement learning (Brochu et al. 2009), and big data applications (Shahriari et al. 2016).”
Selecting Near-Optimal Learners via Incremental Data Allocation (2015, background): “…optimization, where one searches for novel conﬁgurations of algorithms to improve performance [5–7 , 28] A recent non-frequentist approach [19] takes a Bayesian view of multi-armed bandits, applicable especially when the number of arms exceeds the number of allowed evaluations, and…”
An Entropy Search Portfolio for Bayesian Optimization (2014, methodology): “This gives us an important extension of Thompson sampling, a popular bandit strategy that has so far been conﬁned to the multi-armed bandit setting (discrete space X ) [7, 15].”
Bayesian optimization for conditional hyperparameter spaces (2017, background): “Bayesian optimization with Gaussian Processes (GPs) and conditional hyperparameters have been considered in [1, 5], but appear to be inferior to TPE and SMAC with random forests as a surrogate model on the AutoWEKA benchmark [6].”
Closed-loop optimization of fast-charging protocols for batteries with machine learning (2020, methodology): “This algorithm (Methods and Supplementary Discussion 2) builds on the prior work of Hoffman et al. 10 and Grover et al. 11 .”
Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (2017, methodology): “Another interesting recent proposition is the use of bandit optimization techniques for automatic machine learning (Hoffman et al. 2014).”
Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters (2014, background): “Bayesian optimisation has become an important area of research and development in the field of machine learning, as evidenced by recent media coverage [8] and a blossoming range of applications to interactive user-interfaces [4], robotics [16, 19], environmental monitoring [18], information extracti”
Heteroscedastic Treed Bayesian Optimisation (2014, background): “In this section, we give a brief overview of Bayesian optimization as well as a brief survey on Heteroscedastic Gaussian processes.”
A new acquisition function for Bayesian optimization based on the moment-generating function (2017, background): “There are some other, less popular acquisition functions: BayesGap [23] and UGap [24], which are gap-based exploration approaches.”
A review of automatic selection methods for machine learning algorithms and hyper-parameter values (2016, methodology): “2001) [sometimes each with a predetermined set of combinations of hyper-parameter values (Hoffman et al. 2014; Sabharwal et al. 2016)] or hyperparameter values (Wang et al.”