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Found 10 papers, 5 papers with code
High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy
To address this issue, a promising solution is to use a local search strategy that partitions the search domain into local regions with high likelihood of containing the global optimum, and then use BO to optimize the objective function within these regions.
Provably Efficient Bayesian Optimization with Unbiased Gaussian Process Hyperparameter Estimation
Our method uses a multi-armed bandit technique (EXP3) to add random data points to the BO process, and employs a novel training loss function for the GP hyperparameter estimation process that ensures unbiased estimation from the observed data.
Uncertainty-Aware Performance Prediction for Highly Configurable Software Systems via Bayesian Neural Networks
We also develop a novel uncertainty calibration technique to ensure the reliability of the confidence intervals generated by a Bayesian prediction model.
An Efficient Framework for Monitoring Subgroup Performance of Machine Learning Systems
Particularly importance is the problem of monitoring the performance of machine learning systems across all the data subgroups (subpopulations).
ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms
Machine learning models are being used extensively in many important areas, but there is no guarantee a model will always perform well or as its developers intended.
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
High-dimensional black-box optimisation remains an important yet notoriously challenging problem.
High Dimensional Level Set Estimation with Bayesian Neural Network
In particular, we consider two types of LSE problems: (1) \textit{explicit} LSE problem where the threshold level is a fixed user-specified value, and, (2) \textit{implicit} LSE problem where the threshold level is defined as a percentage of the (unknown) maximum of the objective function.
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
To this end, we propose a novel BO algorithm which expands (and shifts) the search space over iterations based on controlling the expansion rate thought a hyperharmonic series.
Distributionally Robust Bayesian Quadrature Optimization
We adopt the distributionally robust optimization perspective to this problem by maximizing the expected objective under the most adversarial distribution.
Bayesian Optimization with Unknown Search Space
Applying Bayesian optimization in problems wherein the search space is unknown is challenging.
