2 citations · 4 across the 17 of their papers we have counts for
7 papers · 1 filter
Regret-Based -optimal Stopping Criteria for Bayesian Optimization
Haowei Wang, Jingyi Wang, Qiyu Wei
Bayesian optimization (BO) is a widely used iterative black-box optimization method that utilizes Gaussian process (GP) surrogate models. In practice, BO is typically terminated af…
Counterfactual Credit Guided Bayesian Optimization
Qiyu Wei, Haowei Wang, Richard Allmendinger +1
Bayesian optimization has emerged as a prominent methodology for optimizing expensive black-box functions by leveraging Gaussian process surrogates, which focus on capturing the gl…
On Improved Regret Bounds In Bayesian Optimization with Gaussian Noise
Jingyi Wang, Haowei Wang, Cosmin G. Petra +1
Bayesian optimization (BO) with Gaussian process (GP) surrogate models is a powerful black-box optimization method. Acquisition functions are a critical part of a BO algorithm as t…
Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and Specificity
Shixiong Wang, Haowei Wang, Xinke Li +1
Trustworthy machine learning aims at combating distributional uncertainties in training data distributions compared to population distributions. Typical treatment frameworks includ…
Distributional Robustness Bounds Generalization Errors
Shixiong Wang, Haowei Wang
Bayesian methods, distributionally robust optimization methods, and regularization methods are three pillars of trustworthy machine learning combating distributional uncertainty, e…
A model aggregation approach for high-dimensional large-scale optimization
Haowei Wang, Ercong Zhang, Szu Hui Ng +1
Bayesian optimization (BO) has been widely used in machine learning and simulation optimization. With the increase in computational resources and storage capacities in these fields…
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization
Shouri Hu, Haowei Wang, Zhongxiang Dai +2
The expected improvement (EI) is one of the most popular acquisition functions for Bayesian optimization (BO) and has demonstrated good empirical performances in many applications…