no code implementations • 1 Feb 2024 • Ruihan Zhou, L. Jeff Hong, Yijie Peng
We introduce AlphaRank, an artificial intelligence approach to address the fixed-budget ranking and selection (R&S) problems.
no code implementations • 29 Nov 2023 • L. Jeff Hong, Yanxi Hou, Qingkai Zhang, Xiaowei Zhang
In this paper, we propose a new metamodeling concept, called generative metamodeling, which aims to construct a "fast simulator of the simulator".
no code implementations • 12 Oct 2022 • Weihuan Huang, Nifei Lin, L. Jeff Hong
We then develop an importance-sampling inspired estimator under the delta-gamma approximations to the portfolio losses, and we show that the rate of convergence of the estimator is $n^{-1/2}$.
no code implementations • 15 Jan 2022 • Tan Wan, L. Jeff Hong
Many large-scale production networks include thousands types of final products and tens to hundreds thousands types of raw materials and intermediate products.
no code implementations • 17 Sep 2020 • Wenhao Li, Ningyuan Chen, L. Jeff Hong
Our algorithm achieves the regret $\tilde{O}(T^{(d_x^*+d_y+1)/(d_x^*+d_y+2)})$, where $d_x^*$ is the effective covariate dimension.
1 code implementation • 1 Aug 2020 • L. Jeff Hong, Weiwei Fan, Jun Luo
In this paper, we briefly review the development of ranking-and-selection (R&S) in the past 70 years, especially the theoretical achievements and practical applications in the last 20 years.
Optimization and Control Methodology
no code implementations • 15 Jul 2019 • Wenhao Li, Ningyuan Chen, L. Jeff Hong
The literature has shown that for Lipschitz-continuous functions, the optimal regret is $\tilde{O}(T^{\frac{d_x+d_y+1}{d_x+d_y+2}})$, where $d_x$ and $d_y$ are the dimensions of contexts and arms, and thus suffers from the curse of dimensionality.
no code implementations • 7 Oct 2017 • Haihui Shen, L. Jeff Hong, Xiaowei Zhang
The goal of ranking and selection with covariates (R&S-C) is to use simulation samples to obtain a selection policy that specifies the best alternative with certain statistical guarantee for subsequent individuals upon observing their covariates.