Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach

By Andreas Krause, Carlos Guestrin. 2007

260 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Making deep neural networks right for the right scientific reasons by interacting with their explanations (2020, background): “Prototypical active learners include max-margin [43] and Bayesian approaches [44]; recently, deep variants have been proposed [45].”
Machine learning of linear differential equations using Gaussian processes (2017, background): “Although not pursued here, this information is very useful in designing a data acquisition plan, often referred to as active learning [12,23,28], that can be used to optimally enhance our knowledge about the parametric linear equation under consideration.”
Safe Exploration for Active Learning with Gaussian Processes (2015, methodology): “Here, recall from Krause and Guestrin (2007) that determining the hyperparameters in advance is similar to defining a grid over the whole input space X ⊂ R.”
Robust Submodular Observation Selection (2008, methodology): “For [L], we train a nonstationary Gaussian Process using data from a single scan of the lake by the NIMS sensor node, using a method described by Krause and Guestrin (2007b).”
Learning under Concept Drift: an Overview (2010, background): “In [86] the authors use active learning of non stationary Gaussian process for river monitoring.”
Explanatory Interactive Machine Learning (2019, background): “Prototypical active learners include max-margin [37] and Bayesian approaches [17]; recently, deep variants have been proposed [12].”
On-line policy learning and adaptation for real-time personalization of an artificial pancreas (2015, background): “Hence, only a relevant part of the state space will be explored while the exploitation–exploration dilemma of the RL problem is addressed (Krause & Guestrin, 2007).”
Efficient Informative Sensing using Multiple Robots (2014, methodology): “…which will allow us to obtain such an algorithm is that the mutual information (and several other notions of informativeness (as discussed in Krause and Guestrin, 2007) satisfies submodularity, an intuitive diminishing returns property - making a new observation helps more if we have made…”