6 papers
Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search
Nuojin Cheng, Alireza Doostan, Stephen Becker
Efficient optimization remains a fundamental challenge across numerous scientific and engineering domains, especially when objective function and gradient evaluations are computati…
A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization
Nuojin Cheng, Leonard Papenmeier, Stephen Becker +1
Bayesian optimization is a widely used method for optimizing expensive black-box functions, with Expected Improvement being one of the most commonly used acquisition functions. In…
Fast algorithms for least square problems with Kronecker lower subsets
Osman Asif Malik, Yiming Xu, Nuojin Cheng +3
While leverage score sampling provides powerful tools for approximating solutions to large least squares problems, the cost of computing exact scores and sampling often prohibits p…
Langevin Bi-fidelity Importance Sampling for Failure Probability Estimation
Nuojin Cheng, Alireza Doostan
Estimating failure probability is a key task in the field of uncertainty quantification. In this domain, importance sampling has proven to be an effective estimation strategy; howe…
Variational Entropy Search for Adjusting Expected Improvement
Nuojin Cheng, Stephen Becker
Bayesian optimization is a widely used technique for optimizing black-box functions, with Expected Improvement (EI) being the most commonly utilized acquisition function in this do…
Exploring Exploration in Bayesian Optimization
Leonard Papenmeier, Nuojin Cheng, Stephen Becker +1
A well-balanced exploration-exploitation trade-off is crucial for successful acquisition functions in Bayesian optimization. However, there is a lack of quantitative measures for e…