Search Results for author:
Found 6 papers, 4 papers with code
A Corrected Expected Improvement Acquisition Function Under Noisy Observations
Sequential maximization of expected improvement (EI) is one of the most widely used policies in Bayesian optimization because of its simplicity and ability to handle noisy observations.
Confidence-aware Personalized Federated Learning via Variational Expectation Maximization
A global model is introduced as a latent variable to augment the joint distribution of clients' parameters and capture the common trends of different clients, optimization is derived based on the principle of maximizing the marginal likelihood and conducted using variational expectation maximization.
Meta-Cal: Well-controlled Post-hoc Calibration by Ranking
In this paper, we introduce two constraints that are worth consideration in designing a calibration map for post-hoc calibration.
Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation
Bayesian optimization (BO) is a sample-efficient global optimization algorithm for black-box functions which are expensive to evaluate.
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization
Bayesian optimization (BO) is a sample-efficient global optimization algorithm for black-box functions which are expensive to evaluate.
A Bayesian Optimization Framework for Neural Network Compression
In this work, we develop a general Bayesian optimization framework for optimizing functions that are computed based on U-statistics.
