activity
20202026
most citedLearning to Order for Inventory Systems with Lost Sales and Uncertain Supplies

14 citations · 45 across the 11 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

Online Semi-infinite Linear Programming: Efficient Algorithms via Function Approximation

Yiming Zong, Jiashuo Jiang

We consider the dynamic resource allocation problem where the decision space is finite-dimensional, yet the solution must satisfy a large or even infinite number of constraints rev…

cs.LG2026

Non-Stationary Online Resource Allocation: Learning from a Single Sample

Yiding Feng, Jiashuo Jiang, Yige Wang

We study online resource allocation under non-stationary demand with a minimum offline data requirement. In this problem, a decision-maker must allocate multiple types of resources…

cs.LG2023

Constrained Online Two-stage Stochastic Optimization: Near Optimal Algorithms via Adversarial Learning

Jiashuo Jiang

We consider an online two-stage stochastic optimization with long-term constraints over a finite horizon of periods. At each period, we take the first-stage action, observe a m…

cs.LG2022★ 5 cited

Degeneracy is OK: Logarithmic Regret for Network Revenue Management with Indiscrete Distributions

Jiashuo Jiang, Will Ma, Jiawei Zhang

We study the classical Network Revenue Management (NRM) problem with accept/reject decisions and IID arrivals. We consider a distributional form where each arrival must fall un…

cs.LG2022★ 6 cited

Non-stationary Bandits with Knapsacks

Shang Liu, Jiashuo Jiang, Xiaocheng Li

In this paper, we study the problem of bandits with knapsacks (BwK) in a non-stationary environment. The BwK problem generalizes the multi-arm bandit (MAB) problem to model the res…

cs.LG2020★ 11 cited

Online Stochastic Optimization with Wasserstein Based Non-stationarity

Jiashuo Jiang, Xiaocheng Li, Jiawei Zhang

We consider a general online stochastic optimization problem with multiple budget constraints over a horizon of finite time periods. In each time period, a reward function and mult…