2 citations · 4 across the 17 of their papers we have counts for
7 papers · 1 filter
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…
Bayesian Optimization with Expected Improvement: No Regret and the Choice of Incumbent
Jingyi Wang, Haowei Wang, Szu Hui Ng +1
Expected improvement (EI) is one of the most widely used acquisition functions in Bayesian optimization (BO). Despite its proven empirical success in applications, the cumulative r…
An Online Non-Stationary Simulation Optimization Approach Based on Regime Switching
Jianglin Xia, Haowei Wang, Songhao Wang +1
Dynamic and evolving operational and economic environments present significant challenges for decision-making. We explore a simulation optimization problem characterized by non-sta…
Convergence Rates of Constrained Expected Improvement
Haowei Wang, Jingyi Wang, Zhongxiang Dai +3
Constrained Bayesian optimization (CBO) methods have seen significant success in black-box optimization with constraints. One of the most commonly used CBO methods is the constrain…
Axiomatic Equilibrium Selection: The Case of Generic Extensive Form Games
Srihari Govindan, Robert B. Wilson
A solution concept that is a refinement of Nash equilibria selects for each finite game a nonempty collection of closed and connected subsets of Nash equilibria as solutions. We im…
Weighted Euclidean Distance Matrices over Mixed Continuous and Categorical Inputs for Gaussian Process Models
Mingyu Pu, Songhao Wang, Haowei Wang +1
Gaussian Process (GP) models are widely utilized as surrogate models in scientific and engineering fields. However, standard GP models are limited to continuous variables due to th…
On the convergence rate of noisy Bayesian Optimization with Expected Improvement
Jingyi Wang, Haowei Wang, Nai-Yuan Chiang +1
Expected improvement (EI) is one of the most widely used acquisition functions in Bayesian optimization (BO). Despite its proven success in applications for decades, important open…