Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations

By Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff Schneider, Barnab'as P'oczos. 2016

164 citations recorded by the source corpus.

Subfields: Optimization & Training, Probabilistic & Bayesian Methods

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Citations in the corpus

Random Search and Reproducibility for Neural Architecture Search (2019, methodology): “These methods use Bayesian optimization [26; 24; 13], performance prediction [15; 10], or multi-armed bandits [22; 28; 29] to adaptively allocate resources to different configurations.”
Neural Architecture Search with Bayesian Optimisation and Optimal Transport (2018, methodology): “[17] Kirthevasan Kandasamy, Gautam Dasarathy, Junier B Oliva, Jeff Schneider, and Barnabás Póczos.”
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization (2016, methodology): “” Several hybrid methods combining adaptive configuration selection and evaluation have also been introduced (Swersky et al., 2013, 2014; Domhan et al., 2015; Kandasamy et al., 2016; Klein et al., 2017a; Golovin et al., 2017).”
Multi-fidelity Bayesian Optimisation with Continuous Approximations (2017, methodology): “We compare BOCA to the following four baselines: (i) GP-UCB , (ii) the GP-EI criterion in BO (Jones et al., 1998), (iii) MF-GP-UCB (Kandasamy et al., 2016a) and (iv) MF-SKO , the multi-ﬁdelity sequential kriging optimisation method from Huang et al. (2006).”
The Multi-fidelity Multi-armed Bandit (2016, background): “4.1 Regret Bound for MF-UCB Recall that N = ∑M m=1Q (m) N is the total (random) number of plays by a multi-fidelity strategy within capital Λ.”
Multi-Fidelity Black-Box Optimization with Hierarchical Partitions (2018, methodology): “Kandasamy et al. (2016c) propose and analyse a UCB style multi-fidelity algorithm for the K-armed bandit setting assuming a finite number of approximations to the K arms.”
Multi-fidelity Gaussian Process Bandit Optimisation (2016, methodology): “We introduced and studied the multi-fidelity bandit problem under Gaussian Process assumptions which builds on our work on multi-fidelity K-armed bandits (Kandasamy et al., 2016b).”
A Tutorial on Bayesian Optimization (2018, unknown): “Work on multi-ﬁdelity optimization includes Huang et al. (2006); S´obester et al. (2004); Forrester et al. (2007); McLeod et al. (2017); Kandasamy et al. (2016).”
Parallelised Bayesian Optimisation via Thompson Sampling (2018, background): “[25] Kirthevasan Kandasamy, Gautam Dasarathy, Junier Oliva, Jeff Schenider, and Barnabás Póczos.”
Review of multi-fidelity models (2016, background): “Many MFMs can harness (or can be extended to) more than two fidelities, as evidenced by the works of Huang et al. (2006) [95], Forrester et al. (2007) [71], Qian et al. (2008) [174], and Goh et al. (2013) [80], Kandasamy et al. (2016) [104] and others.”
Multi-Information Source Optimization (2016, methodology): “We also face this problem in robotics: we can evaluate a parameterized robot control policy in simulation, in a laboratory, or in a field test [15].”