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Found 9 papers, 5 papers with code
Efficient computation of the Knowledge Gradient for Bayesian Optimization
Bayesian optimization is a powerful collection of methods for optimizing stochastic expensive black box functions.
A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review
We propose the Mallows-Binomial model to close this gap, which combines a Mallows' $\phi$ ranking model with Binomial score models through shared parameters that quantify object quality, a consensus ranking, and the level of consensus between judges.
Factorized Gaussian Process Variational Autoencoders
Variational autoencoders often assume isotropic Gaussian priors and mean-field posteriors, hence do not exploit structure in scenarios where we may expect similarity or consistency across latent variables.
Scalable Gaussian Process Variational Autoencoders
Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors.
Sparse Gaussian Process Variational Autoencoders
Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering.
Bayesian Optimisation vs. Input Uncertainty Reduction
Particularly when performing simulation optimisation to find an optimal solution, the uncertainty in the inputs significantly affects the quality of the found solution.
Practical Bayesian Optimization of Objectives with Conditioning Variables
Bayesian optimization is a class of data efficient model based algorithms typically focused on global optimization.
Bayesian Optimization Allowing for Common Random Numbers
Bayesian optimization is a powerful tool for expensive stochastic black-box optimization problems such as simulation-based optimization or machine learning hyperparameter tuning.
Scalable Global Optimization via Local Bayesian Optimization
This motivates the design of a local probabilistic approach for global optimization of large-scale high-dimensional problems.
