Bayesian optimization for sensor set selection

By Roman Garnett, Michael A. Osborne, Stephen J. Roberts. 2010

178 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Ocean Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

In Ocean Engineering

Citations in the corpus

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, methodology): “Given data D and a likelihood model p ( D | w ) , we can then infer a posterior distribution p ( w | D ) using Bayes’ rule: This posterior represents our updated beliefs about w after observing data D .”
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].”
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning (2010, methodology): “For example, it is possible to derive analytical expressions for the two-step ahead expected improvement [Ginsbourger et al., 2008] and multistep Bayesian optimization [Garnett et al., 2010b].”
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes (2017, background): “…of black-box functions include automatic tuning machine learning hyper-parameters (Snoek et al., 2012), ﬁnding optimal control parameters in robotics (Lizotte et al., 2007), optimal weather sensor placement (Garnett et al., 2010) or the optimization search strategies (Cornejo-Bueno et al., 2018).”
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search (2020, methodology): “Bayesian optimization (BO) optimizes the black-box function by sequentially evaluating it (Garnett et al., 2010; Shahriari et al., 2016; Nguyen & Osborne, 2020).”
Bayesian Multi-Scale Optimistic Optimization (2014, background): “Examples of acquisition functions include probability of improvement, expected improvement, Bayesian expected losses, upper confidence bounds (UCB), and dynamic portfolios of these (Močkus, 1982; Jones, 2001; Garnett et al., 2010; Srinivas et al., 2010; Chen et al., 2012; Hoffman et al., 2011).”
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): “, 2014), information extraction (Wang and de Freitas, 2014), sensor networks (Garnett et al., 2010; Srinivas et al., 2010), adaptive Monte Carlo (Wang et al.”
Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations (2012, background): “We refer the reader to (Močkus, 1982; Schonlau et al., 1998; Gramacy et al., 2004; Brochu et al., 2007; Lizotte, 2008; Martinez–Cantin et al., 2009; Garnett et al., 2010) for practical examples.”
Recent Advances in Bayesian Optimization (2022, background): “real-world problems, including materials design and discovery [54], sensor network [59], financial industry [68], and experimental design [123].”
Anomaly Detection and Removal Using Non-Stationary Gaussian Processes (2015, background): “Current applications are in diverse fields such as geophysics, medical imaging, multi-sensor fusion [5] and sensor placement [1].”
Bayesian optimization for learning gaits under uncertainty (2015, methodology): “Bayesian optimization has been successfully applied to sensor-set selection [8], gait optimization for quadrupeds [9] and snake robots [10] and automatic algorithm conﬁguration [11,12].”
Bayesian Optimization over Sets (2019, methodology): “In BO, [10] suggests a method to ﬁnd a set that produces a global minimum with respect to discrete sets, each of which is an element of power set of entire set.”
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].”
Stable Bayesian optimization (2018, methodology): “Apart from hyperparameter tuning, Bayesian optimization has also been used for optimal sensor placement [5], for gait design [12] and optimal path planning [13], etc.”