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Active learning: Problem settings and recent developments
H Hino - ar** condition
Bayesian optimization (BO) is a sample-efficient method for global optimization of
expensive, noisy, black-box functions using probabilistic methods. The performance of a BO …
expensive, noisy, black-box functions using probabilistic methods. The performance of a BO …
Automatic termination for hyperparameter optimization
Bayesian optimization (BO) is a widely popular approach for the hyperparameter
optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising …
optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising …
[HTML][HTML] The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI
Functional neuroimaging typically explores how a particular task activates a set of brain
regions. Importantly though, the same neural system can be activated by inherently different …
regions. Importantly though, the same neural system can be activated by inherently different …
Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization
Understanding the unique contributions of frontoparietal networks (FPN) in cognition is
challenging because they overlap spatially and are co-activated by diverse tasks …
challenging because they overlap spatially and are co-activated by diverse tasks …
Optimization, fast and slow: optimally switching between local and Bayesian optimization
M McLeod, S Roberts… - … Conference on Machine …, 2018 - proceedings.mlr.press
We develop the first Bayesian Optimization algorithm, BLOSSOM, which selects between
multiple alternative acquisition functions and traditional local optimization at each step. This …
multiple alternative acquisition functions and traditional local optimization at each step. This …
Constrained efficient global multidisciplinary design optimization using adaptive disciplinary surrogate enrichment
Surrogate-based optimization has become a popular approach for solving problems with
computationally expensive disciplinary solvers. Recently, the Efficient Global …
computationally expensive disciplinary solvers. Recently, the Efficient Global …
Stop** criterion for active learning based on deterministic generalization bounds
H Ishibashi, H Hino - International Conference on Artificial …, 2020 - proceedings.mlr.press
Active learning is a framework in which the learning machine can select the samples to be
used for training. This technique is promising, particularly when the cost of data acquisition …
used for training. This technique is promising, particularly when the cost of data acquisition …