Research

Selected working papers

Listed in reverse chronological order

  • Regret Minimization and Statistical Inference in Online Decision Making with High-dimensional Covariates,
    Congyuan Duan, Wanteng Ma, Jiashuo Jiang, Dong Xia [Paper]

  • Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control,
    Zifan Liu, Xinran Li, Shibo Chen, Gen Li, Jiashuo Jiang, Jun Zhang [Paper]

  • Instance-dependent Sample Complexity for Constrained Markov Decision Process,
    Jiashuo Jiang, Yinyu Ye [Paper]

  • Constrained Online Two-stage Stochastic Optimization: Algorithms with (and without) Predictions,
    Piao Hu, Jiashuo Jiang, Guodong Lyu, Hao Su [Paper]

  • Constant Approximation for Network Revenue Management with Markovian-correlated Customer Arrivals,
    Jiashuo Jiang [Paper]

  • Online Resource Allocation with Stochastic Resource Consumption,
    Jiashuo Jiang, Jiawei Zhang [Paper]

2025

  • Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control,
    Zifan Liu, Xinran Li, Shibo Chen, Gen Li, Jiashuo Jiang, Jun Zhang [Paper]
    The 28th International Conference on Artificial Intelligence and Statistics (AISTATS 2025).

  • Degeneracy is OK: Logarithmic Regret for Network Revenue Management with Indiscrete Distributions,
    Jiashuo Jiang, Will Ma, Jiawei Zhang [Paper]
    Operations Research, 2025.

  • Tightness without Counterexamples: A New Approach and New Results for Prophet Inequalities,
    Jiashuo Jiang, Will Ma, Jiawei Zhang [Paper]
    Mathematics of Operations Research, 2025.

2024

  • Scheduling in LLM Inference with Blowed-up Memory Constraints,
    Zijie Zhou, Jiashuo Jiang, Patrick Jaillet, Chara Podimata
    NeurIPS 2024 Workshop on Statistical Frontiers of LLM and Foundation Models.

  • Achieving (1/ε) Sample Complexity for Constrained Markov Decision Process,
    Jiashuo Jiang, Yinyu Ye [Paper]
    Thirty-eighth Conference on Neural Information Processing Systems (NeurIPS 2024).

  • High-dimensional Linear Bandits with Knapsacks,
    Wanteng Ma, Dong Xia, Jiashuo Jiang [Paper]
    2024 International Conference on Machine Learning (ICML 2024).

  • Online Stochastic Optimization with Wasserstein Based Non-stationarity,
    Jiashuo Jiang, Xiaocheng Li, Jiawei Zhang [Paper]
    Management Science, 2024.

  • Tight Guarantees for Multi-unit Prophet Inequalities and Online Stochastic Knapsack,
    Jiashuo Jiang, Will Ma, Jiawei Zhang [Paper]
    Operations Research, 2024.

    Finalist for the George Nicholson Student Paper Competition

    Finalist for the Jeff McGill Student Paper Competition

2023

  • Tightness without Counterexamples: A New Approach and New Results for Prophet Inequalities,
    Jiashuo Jiang, Will Ma, Jiawei Zhang [Paper]
    The Twenty-Fourth ACM Conference on Economics and Computation (EC 2023).

  • Learning to Order for Inventory Systems with Lost Sales and Uncertain Supplies
    Boxiao Chen, Jiashuo Jiang, Jiawei Zhang, Zhengyuan Zhou [Paper]
    Management Science, 2023.

2022

  • Achieving High Individual Service-Levels without Safety Stock? Optimal Rationing Policy of Pooled Resources
    Jiashuo Jiang, Shixin Wang, Jiawei Zhang [Paper]
    Operations Research, 2022.

  • Non-stationary Bandits with Knapsacks,
    Shang Liu, Jiashuo Jiang, Xiaocheng Li [Paper]
    Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022).

  • Tight Guarantees for Multi-unit Prophet Inequalities and Online Stochastic Knapsack,
    Jiashuo Jiang, Will Ma, Jiawei Zhang [Paper]
    ACM-SIAM Symposium on Discrete Algorithms (SODA 2022) [Link].