2 citations · 4 across the 17 of their papers we have counts for
20 papers
Principled Design of Expected Improvement for Continuous Simulation Optimization
Songhao Wang, Jianglin Xia, Haowei Wang +1
Expected improvement based on stochastic kriging (SK) surrogate is widely used in continuous simulation optimization, but unknown heteroscedastic noise makes three basic design cho…
Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models
Yilin Zheng, Haowei Wang, Szu Hui Ng +1
Bayesian optimization (BO) is a widely used approach for black-box optimization that uses a Gaussian process (GP) as a surrogate and guides sequential evaluations via an acquisitio…
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…
Practical Efficient Global Optimization is No-regret
Jingyi Wang, Haowei Wang, Nai-Yuan Chiang +3
Efficient global optimization (EGO) is one of the most widely used noise-free Bayesian optimization algorithms.It comprises the Gaussian process (GP) surrogate model and expected i…
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…
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…
Robust Waveform Design for Integrated Sensing and Communication
Shixiong Wang, Wei Dai, Haowei Wang +1
Integrated sensing and communication (ISAC), which enables hardware, resources (e.g., spectra), and waveforms sharing, is becoming a key feature in future-generation communication…
A Budget-Adaptive Allocation Rule for Optimal Computing Budget Allocation
Zirui Cao, Haowei Wang, Ek Peng Chew +2
Simulation-based ranking and selection (R&S) is a popular technique for optimizing discrete-event systems (DESs). It evaluates the mean performance of system designs by simulation…
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…
Enhanced Global Optimization with Parallel Global and Local Structures
Haowei Wang, Songhao Wang, Qun Meng +1
In practice, objective functions of real-time control systems can have multiple local minimums or can dramatically change over the function space, making them hard to optimize. To…
A Data-Driven Bayesian Nonparametric Approach for Black-Box Optimization
Haowei Wang, Xun Zhang, Szu Hui Ng +1
We present a data-driven Bayesian nonparametric approach for global optimization (DaBNO) of stochastic black-box function. The function value depends on the distribution of a rando…