Gaussian Processes for Global Optimization

By Roman Garnett, Michael A. Osborne, Stephen J. Roberts. 2008

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Citations in the corpus

Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, methodology): “In academia, it is impacting a wide range of areas, including interactive user-interfaces [26], robotics [101], [110], environmental monitoring [106], information extraction [158], combinatorial optimisation [79], [159], automatic machine learning [16], [143], [148], [151], [72], sensor networks [55”
Freeze-Thaw Bayesian Optimization (2014, background): “Recent work has developed compelling theoretical convergence guarantees [6, 7, 8], more rigorous methodologies [9, 10] and methods tailored to speciﬁc applications such as hyperparameter optimization [11, 4, 3] and sensor set selection [12].”
Input Warping for Bayesian Optimization of Non-Stationary Functions (2014, background): “Recent work in machine learning has revisited the idea of Bayesian optimization [e.g., Osborne et al., 2009, Brochu et al., 2010, Srinivas et al., 2010, Hutter et al., 2011, Bergstra et al., 2011, Bull, 2011, Snoek et al., 2012, Hennig and Schuler, 2012] in large part due to advances in the ability…”
Entropy Search for Information-Efficient Global Optimization (2011, background): “But it is useful to pause at this point to contrast this deﬁnition with other concepts of optimization.”
Gaussian Processes for Data-Efficient Learning in Robotics and Control (2015, neutral): “[10] and Bayesian optimization [9], [33], [41], [51] to avoid getting stuck in local minima.”
Bayesian Optimization in a Billion Dimensions via Random Embeddings (2013, background): “…optimization, and we refer readers unfamiliar with the topic to more tutorial treatments (Brochu et al., 2009; Jones et al., 1998; Jones, 2001; Lizotte et al., 2011; Moˇckus, 1994; Osborne et al., 2009) and recent theoretical results (Srinivas et al., 2010; Bull, 2011; de Freitas et al., 2012).”
Bayesian optimization for learning gaits under uncertainty (2015, methodology): “Bayesian optimization (BO) is a global optimization method [4,6,7] based on response surface (i.e., surrogate model).”
Bayesian Optimization with an Empirical Hardness Model for approximate Nearest Neighbour Search (2014, background): “Here we cover Bayesian Optimization with Gaussian Processes very briefly, and direct the unfamiliar reader to more tutorial treatments of the subject [4, 14, 16].”
Probabilistic Matrix Factorization for Automated Machine Learning (2017, background): “Correspondence to: Nicolo Fusi < fusi@microsoft.com > , Rishit Sheth < rishit.sheth@tufts.edu > . a promising alternative to these approaches (Srinivas et al., 2009; Hutter et al., 2011; Osborne et al., 2009; Bergstra et al., 2011; Snoek et al., 2012; Bergstra et al., 2013).”
Quantum-assisted quantum compiling (2018, methodology): “Specifically, we make use of the gp_minimize routine in the scikit-optimize Python library [45], which is a gradient-free optimization routine that performs Bayesian optimization using Gaussian processes [46, 47].”
A benchmark of kriging-based infill criteria for noisy optimization (2013, methodology): “The variety of applications has resulted in different approaches for Noisy Kriging-based Optimization (NKO) over the last years [10, 15, 39, 25, 26, 38, 35].”
An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion (2014, methodology): “Bayesian Optimization Bayesian optimization is an iterative model-based global optimization method [7, 5, 11, 1].”
A Tutorial on Bayesian Optimization (2018, methodology): “In other related work in this area, Osborne et al. (2009) proposed using gradient information to improve conditioning of the covariance matrix in GP regression, and Ahmed et al. (2016) proposed a method for choosing a single directional derivative to retain when observing gradients to improve the…”
Regret Bounds for Gaussian Process Bandit Problems (2010, unknown): “Due to the ﬂexibility and power of GPs, the approach has sparked considerable interest (Schonlau, 1997; Jones et al., 1998; Jones, 2001; Ginsbourger and Riche, 2009; Osborne et al., 2009).”
Automated Machine Learning using Stochastic Algorithm Tuning (2014, methodology): “Marginalisation of the hyper-parameters as recommended in [7] (Bayesian Quadrature (BQ)) or [17] (Markov chain Monte-Carlo (MCMC)) may reduce the need to pre-sample, at the expense of additional computational load.”