9 citations · 25 across the 6 of their papers we have counts for
6 papers
A Corrected Expected Improvement Acquisition Function Under Noisy Observations
Han Zhou, Xingchen Ma, Matthew B Blaschko
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 observat…
Inverse Lithography Physics-informed Deep Neural Level Set for Mask Optimization
Xing-Yu Ma, Shaogang Hao
As the feature size of integrated circuits continues to decrease, optical proximity correction (OPC) has emerged as a crucial resolution enhancement technology for ensuring high pr…
Confidence-aware Personalized Federated Learning via Variational Expectation Maximization
Junyi Zhu, Xingchen Ma, Matthew B. Blaschko
Federated Learning (FL) is a distributed learning scheme to train a shared model across clients. One common and fundamental challenge in FL is that the sets of data across clients…
Meta-Cal: Well-controlled Post-hoc Calibration by Ranking
Xingchen Ma, Matthew B. Blaschko
In many applications, it is desirable that a classifier not only makes accurate predictions, but also outputs calibrated posterior probabilities. However, many existing classifiers…
Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation
Xingchen Ma, Matthew B. Blaschko
Bayesian optimization (BO) is a sample-efficient global optimization algorithm for black-box functions which are expensive to evaluate. Existing literature on model based optimizat…
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization
Xingchen Ma, Matthew B. Blaschko
Bayesian optimization (BO) is a sample-efficient global optimization algorithm for black-box functions which are expensive to evaluate. Existing literature on model based optimizat…