Zobrazeno 1 - 10
of 299
pro vyhledávání: '"Xie Weijun"'
In optimal experimental design, the objective is to select a limited set of experiments that maximizes information about unknown model parameters based on factor levels. This work addresses the generalized D-optimal design problem, allowing for nonli
Externí odkaz:
http://arxiv.org/abs/2411.01405
Autor:
Deng, Haoyun, Xie, Weijun
We study stochastic mixed integer programs with both first-stage and recourse decisions involving mixed integer variables. A new family of Lagrangian cuts, termed ``ReLU Lagrangian cuts," is introduced by reformulating the nonanticipativity constrain
Externí odkaz:
http://arxiv.org/abs/2411.01229
The training of classification models for fault diagnosis tasks using geographically dispersed data is a crucial task for original equipment manufacturers (OEMs) seeking to provide long-term service contracts (LTSCs) to their customers. Due to privac
Externí odkaz:
http://arxiv.org/abs/2410.03877
Publikováno v:
Frontiers in Physiology, Vol 13 (2022)
Purpose: This study aimed to explore the characteristics of resting energy expenditure (REE) and lipid metabolism during incremental load exercise in obese children and adolescents with insulin resistance (IR) to provide evidence for exercise interve
Externí odkaz:
https://doaj.org/article/945a001211d849dbb912eef8def78461
We consider a feature-based personalized pricing problem in which the buyer is strategic: given the seller's pricing policy, the buyer can augment the features that they reveal to the seller to obtain a low price for the product. We model the seller'
Externí odkaz:
http://arxiv.org/abs/2408.08738
Incorporating energy storage systems (ESS) into power systems has been studied in many recent works, where binary variables are often introduced to model the complementary nature of battery charging and discharging. A conventional approach for these
Externí odkaz:
http://arxiv.org/abs/2402.04406
A traditional stochastic program under a finite population typically seeks to optimize efficiency by maximizing the expected profits or minimizing the expected costs, subject to a set of constraints. However, implementing such optimization-based deci
Externí odkaz:
http://arxiv.org/abs/2402.01872
Autor:
Jiang, Nan, Xie, Weijun
Distributionally Favorable Optimization (DFO) is an important framework for decision-making under uncertainty, with applications across fields such as reinforcement learning, online learning, robust statistics, chance-constrained programming, and two
Externí odkaz:
http://arxiv.org/abs/2401.17899
The classical Canonical Correlation Analysis (CCA) identifies the correlations between two sets of multivariate variables based on their covariance, which has been widely applied in diverse fields such as computer vision, natural language processing,
Externí odkaz:
http://arxiv.org/abs/2401.00308
We consider the problem of learning fair policies for multi-stage selection problems from observational data. This problem arises in several high-stakes domains such as company hiring, loan approval, or bail decisions where outcomes (e.g., career suc
Externí odkaz:
http://arxiv.org/abs/2312.13173