Adaptive MCMC with Bayesian Optimization

By Nimalan Mahendran, Ziyun Wang, Firas Hamze, Nando de Freitas. 2012

80 citations recorded by the source corpus.

Subfields: Optimization & Training, Probabilistic & Bayesian Methods

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Citations in the corpus

A-NICE-MC: Adversarial Training for MCMC (2017, background): “Defining an objective to improve the performance of MCMC that can be easily optimized in practice over a large parameter space is itself a difficult problem [12, 13].”
Practical Bayesian Optimization of Machine Learning Algorithms (2012, neutral): “Bayesian optimization strategies have also been used to tune the parameters of Markov chain Monte Carlo algorithms [8].”
Scalable Bayesian Optimization Using Deep Neural Networks (2015, background): “…and transfer optimization (Krause & Ong, 2011; Swersky et al., 2013; Bar-denet et al., 2013) and the application to diverse tasks such as sensor set selection (Garnett et al., 2010), the tuning of adaptive Monte Carlo (Mahendran et al., 2012) and robotic gait control (Calandra et al., 2014b).”
Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, background): “Returning to the drug application, we can imagine the effectiveness of different drugs (arms on the bandit) as being determined by a function f that takes an index a ∈ 1 , . . . , K and returns a Bernoulli parameter in the interval (0 , 1) .”
An Entropy Search Portfolio for Bayesian Optimization (2014, methodology): “…of these techniques include interactive user-interfaces [6, 4], robotics [18, 21], environmental monitoring [20], information extraction [33], combinatorial optimization [16, 34], sensor networks [10, 28], adaptive Monte Carlo [19, 32], experimental design [1], and reinforcement learning [5].”
On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning (2014, methodology): “Bayesian optimization techniques have already been proposed to automate machine learning approaches, 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…”
Bayesian Optimization with Inequality Constraints (2014, methodology): “Mahendran et al. (2012) applies Bayesian optimization to perform adaptive MCMC. Finally, Hoffman et al. (2013) introduce constraints on the number of function evaluations, rather than expensive-to-compute constraints , which we model with cBO.”
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”
High Dimensional Bayesian Optimisation and Bandits via Additive Models (2015, methodology): “Gaussian process bandits and Bayesian optimisation (GPB/ BO) have been successfully applied in many applications such as tuning hyperparameters in learning algorithms (Snoek et al., 2012; Bergstra et al., 2011; Mahendran et al., 2012), robotics (Lizotte et al.”
Bayesian Multi-Scale Optimistic Optimization (2014, methodology): “For example, Snoek et al. (2012) use discretisation, Bardenet & Kégl (2010) use adaptive grids, Brochu et al.”