2 citations · 6 across the 10 of their papers we have counts for
10 papers
Resource-Adaptive Stochastic Gradient Descent for Online Linear Programming without Re-solving
Jiameng Lyu
The growth of large language model (LLM) inference and search services increases the scale of online linear programming problems, motivating computationally efficient algorithms. W…
Resource-Adaptive Primal-Dual Learning for One-Warehouse Multi-Store Systems with Censored Demand
Jiameng Lyu
The one-warehouse multi-store (OWMS) system is a fundamental inventory network in which a nonreplenishable warehouse allocates shared stock across multiple stores over time. Existi…
Learning in Position-Aware Multinomial Logit Bandits: From Multiplicative to General Position Effects
Xi Chen, Shibo Dai, Jiameng Lyu +1
We study the dynamic joint assortment selection and positioning problem, where the attraction of each product depends on both its intrinsic appeal and its display position under a…
Allocating Human Oversight in AI-Enabled Analytics
Zikun Ye, Jiameng Lyu, Rui Tao
Organizations increasingly deploy AI as a low-cost prediction layer in customer-facing decision processes, including demand sensing, service-quality monitoring, product testing, an…
Learning When to Restart: Nonstationary Newsvendor from Uncensored to Censored Demand
Xin Chen, Jiameng Lyu, Shilin Yuan +1
We study nonstationary newsvendor problems under nonparametric demand models and general distributional measures of nonstationarity, addressing the practical challenges of unknown…
A Minibatch-SGD-Based Learning Meta-Policy for Inventory Systems with Myopic Optimal Policy
Jiameng Lyu, Jinxing Xie, Shilin Yuan +1
Stochastic gradient descent (SGD) has proven effective in solving many inventory control problems with demand learning. However, it often faces the pitfall of an infeasible target…
Regret Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems: A General Optimization Perspective
Jiameng Lyu, Shilin Yuan, Bingkun Zhou +1
Numerous existing studies have examined the performance of Sample Average Approximation (SAA) in the fundamental newsvendor problem. Despite these advances, critical gaps remain in…
Stochastic Constrained DRO with a Complexity Independent of Sample Size
Qi Qi, Jiameng Lyu, Kung sik Chan +2
Distributionally Robust Optimization (DRO), as a popular method to train robust models against distribution shift between training and test sets, has received tremendous attention…
Network Revenue Management with Demand Learning and Fair Resource-Consumption Balancing
Xi Chen, Jiameng Lyu, Yining Wang +1
In addition to maximizing the total revenue, decision-makers in lots of industries would like to guarantee balanced consumption across different resources. For instance, in the ret…
Fairness-aware Online Price Discrimination with Nonparametric Demand Models
Xi Chen, Jiameng Lyu, Xuan Zhang +1
Price discrimination, which refers to the strategy of setting different prices for different customer groups, has been widely used in online retailing. Although it helps boost the…