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Batch Active Learning via Coordinated Matching
Most prior work on active learning of classifiers has focused on sequentially selecting one unlabeled example at a time to be labeled in order to reduce the overall labeling effort.
A Lipschitz Exploration-Exploitation Scheme for Bayesian Optimization
The exploration phase aims to select samples that shrink the search space as much as possible.
Budgeted Optimization with Concurrent Stochastic-Duration Experiments
This paper defines a novel problem formulation with the following important extensions: 1) allowing for concurrent experiments; 2) allowing for stochastic experiment durations; and 3) placing constraints on both the total number of experiments and the total experimental time.
Batch Bayesian Optimization via Simulation Matching
Bayesian optimization methods are often used to optimize unknown functions that are costly to evaluate.
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