An experimental methodology for response surface optimization methods

By Daniel J. Lizotte, R. Greiner, Dale Schuurmans. 2011

59 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, methodology): “When gradients are available, or can be cheaply approximated, one can use a multistarted quasiNewton hill-climbing approach [100], [143].”
Bayesian optimization for learning gaits under uncertainty (2015, background): “In [23], it is suggested that ﬁxing the hyperparameters can considerably speed up the convergence of BO.”
Optimization as Estimation with Gaussian Processes in Bandit Settings (2015, background): “Extensive empirical comparisons include [6, 29, 20].”
UvA-DARE (Digital Academic Repository) Bayesian Optimization in High Dimensions via Random Embeddings (2013, unknown): “…Bayesian 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; de Freitas et al. , 2012].”
On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning (2014, background): “…such as MCMC inference [Mahendran et al., 2012, Hamze et al., 2013, Wang et al., 2013a], deep learning [Bergstra et al., 2011], preference learning [Brochu et al., 2007, 2010a], reinforcement learning and control [Martinez-Cantin et al., 2007, Lizotte et al., 2012], and more [Snoek et al., 2012].”
An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion (2014, background): “There are suggestions [8] that ﬁxing the hyperparameters can considerably speed up the convergence of Bayesian optimization.”
Adaptive MCMC with Bayesian Optimization (2012, background): “…the strategy in the complex setting of sampling from constrained, discrete and densely connected probabilistic graphical models where, for each variation of the problem, one needs to adjust the parameters of the proposal mechanism automatically to ensure efficient mixing of the Markov chains.”
Bayesian Multi-Scale Optimistic Optimization (2014, methodology): “Many other acquisition functions have been proposed, but they often yield similar results; see for example the works of Močkus (1982) and Jones (2001).”
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.,…”