5 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…
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…
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…
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…
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…