Publications  

  By Topic / By Time

Recent works:

  1. Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification
    Shang Liu, Zhongze Cai, Guanting Chen, Xiaocheng Li [arXiv]
  2. Understanding the Training and Generalization of Pretrained Transformer for Sequential Decision Making
    Hanzhao Wang, Yu Pan, Fupeng Sun, Shang Liu, Kalyan Talluri, Guanting Chen, Xiaocheng Li [arXiv]
  3. Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach
    Linyu Liu, Yu Pan, Xiaocheng Li, Guanting Chen [arXiv]
  4. Towards Better Statistical Understanding of Watermarking LLMs
    Zhongze Cai, Shang Liu, Hanzhao Wang, Huaiyang Zhong, Xiaocheng Li [arXiv]
  5. Transformer Choice Net: A Transformer Neural Network for Choice Prediction
    Hanzhao Wang, Xiaocheng Li, Kalyan Talluri [arXiv]
  6. Understanding Uncertainty Sampling
    Shang Liu, Xiaocheng Li [arXiv]
  7. Learning to Make Adherence-Aware Advice
    Guanting Chen, Xiaocheng Li, Chunlin Sun, Hanzhao Wang [arXiv]
    ICLR 2024.
  8. When No-Rejection Learning is Optimal for Regression with Rejection
    Xiaocheng Li, Shang Liu, Chunlin Sun, Hanzhao Wang [arXiv]
    AISTATS 2024.
  9. Facilitating Battery Swapping Services for Freight Trucks with Spatial-Temporal Demand Prediction
    Linyu Liu, Zhen Dai, Shiji Song, Xiaocheng Li, Guanting Chen [arXiv]
    NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning.
  10. Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods
    Shang Liu, Zhongze Cai, Xiaocheng Li [arXiv]
    NeurIPS 2023.
  11. Predict-then-Calibrate: A New Perspective of Robust Contextual LP
    Chunlin Sun, Linyu Liu, Xiaocheng Li [arXiv]
    NeurIPS 2023.

Linear Programming & Stochastic Programming

  1. Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming
    Chunlin Sun, Shang Liu, Xiaocheng Li. [arXiv]
    ICML 2023.
  2. Stochastic Inverse Optimization
    John Birge, Xiaocheng Li, Chunlin Sun. [pdf]
  3. Non-stationary Bandits with Knapsacks
    Shang Liu, Jiashuo Jiang, Xiaocheng Li. [arXiv]
    NeurIPS 2022.
  4. Online Bin Packing with Known T
    Shang Liu, Xiaocheng Li
    Major revision at Mathematics of Operations Research. [arXiv]
  5. The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks
    Xiaocheng Li, Chunlin Sun, Yinyu Ye
    ICML 2021 (Long talk). [arXiv]
  6. Online Stochastic Optimization with Wasserstein Based Non-stationarity
    Jiashuo Jiang, Xiaocheng Li, Jiawei Zhang
    Management Science, 2024. [arXiv]
  7. Simple and Fast Algorithm for Binary Integer and Online Linear Programming
    Xiaocheng Li, Chunlin Sun, Yinyu Ye
    NeurIPS 2020 (Long version at Mathematical Programming). [arXiv]
  8. Online Linear Programming: Dual Convergence, New Algorithms, and Regret Bounds
    Xiaocheng Li, Yinyu Ye
    Operations Research, 2021. [arXiv]
  9. Quantile Markov Decision Processes
    Xiaocheng Li, Huaiyang Zhong, Margaret L. Brandeau
    Operations Research, 2021 [arXiv]

Revenue Management

  1. Neural Choice Model: Parameter Estimation and Assortment Optimization
    Hanzhao Wang, Zhongze Cai, Xiaocheng Li, Kalyan Talluri.
    Draft available soon.
  2. Deep Learning for Choice Modeling
    Zhongze Cai, Hanzhao Wang, Kalyan Talluri, Xiaocheng Li. [arXiv]
    Under review.
  3. Learning to Sell a Focal-ancillary Combination
    Hanzhao Wang, Xiaocheng Li, Kalyan Talluri. [arXiv]
    Under review.
  4. Learning from Stochatically Revealed Preference
    John Birge, Xiaocheng Li, Chunlin Sun. [arXiv]
    NeurIPS 2022.
  5. On Dynamic Pricing with Covariates
    Hanzhao Wang, Kalyan Talluri, Xiaocheng Li
    Minor revision at Operations Research (technical note). [arXiv]
  6. An Improved Analysis of LP-based Control for Revenue Management
    Guanting Chen, Xiaocheng Li, Yinyu Ye
    Operations Research (Technical Note), 2022. [arXiv]
    Fairness aspect of the problem: [arXiv]
  7. Dynamic Pricing with External Information and Limited Inventory
    Xiaocheng Li, Zeyu Zheng
    Management Science, 2023. [SSRN]

Applications & Earlier Works

  1. Demand Prediction, Predictive Shipping, and Product Allocation for Large-scale E-commerce
    Xiaocheng Li, Yufeng Zheng, Zhenpeng Zhou, Zeyu Zheng [SSRN]
    M&SOM data-driven research challenge, finalist.
  2. Data-Driven Ranking and Selection: High-Dimensional Covariates and General Dependence
    Xiaocheng Li, Xiaowei Zhang, Zeyu Zheng
    WSC 2018 [Link]
  3. Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans.
    Huaiyang Zhong, Xiaocheng Li, David Lobell, Stefano Ermon, Margaret L. Brandeau
    Environment Systems and Decisions, 38(4), 458-470, 2018 [Link]
  4. Recurrent Autoregressive Networks for Online Multi-Object Tracking
    Kuan Fang, Yu Xiang, Xiaocheng Li, Silvio Savarese
    WACV 2018. [Link]
  5. Optimizing Chemical Reactions with Deep Reinforcement Learning
    Zhenpeng Zhou, Xiaocheng Li, Richard N. Zare
    American Chemical Society (ACS) Central Science 2017, 3(12), 1337-1344. [Link]
  6. Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
    Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, Stefano Ermon
    Winner, Best Solution Prize for 2016 World Bank Big Data Innovation Challenge
    AAAI 2017. [Link]
  7. A Closed-form Expansion Approach for Pricing Discretely Monitored Variance Swaps
    Chenxu Li, Xiaocheng Li (Undergraduate thesis)
    Operations Research Letters, 2015. [Link]

  1. Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification
    Shang Liu, Zhongze Cai, Guanting Chen, Xiaocheng Li [arXiv]
  2. Understanding the Training and Generalization of Pretrained Transformer for Sequential Decision Making
    Hanzhao Wang, Yu Pan, Fupeng Sun, Shang Liu, Kalyan Talluri, Guanting Chen, Xiaocheng Li [arXiv]
  3. Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach
    Linyu Liu, Yu Pan, Xiaocheng Li, Guanting Chen [arXiv]
  4. Towards Better Statistical Understanding of Watermarking LLMs
    Zhongze Cai, Shang Liu, Hanzhao Wang, Huaiyang Zhong, Xiaocheng Li [arXiv]
  5. Transformer Choice Net: A Transformer Neural Network for Choice Prediction
    Hanzhao Wang, Xiaocheng Li, Kalyan Talluri [arXiv]
  6. Understanding Uncertainty Sampling
    Shang Liu, Xiaocheng Li [arXiv]
  7. Learning to Make Adherence-Aware Advice
    Guanting Chen, Xiaocheng Li, Chunlin Sun, Hanzhao Wang [arXiv]
    ICLR 2024.
  8. When No-Rejection Learning is Optimal for Regression with Rejection
    Xiaocheng Li, Shang Liu, Chunlin Sun, Hanzhao Wang [arXiv]
    AISTATS 2024.
  9. Facilitating Battery Swapping Services for Freight Trucks with Spatial-Temporal Demand Prediction
    Linyu Liu, Zhen Dai, Shiji Song, Xiaocheng Li, Guanting Chen [arXiv]
    NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning.
  10. Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods
    Shang Liu, Zhongze Cai, Xiaocheng Li [arXiv]
    NeurIPS 2023.
  11. Predict-then-Calibrate: A New Perspective of Robust Contextual LP
    Chunlin Sun, Linyu Liu, Xiaocheng Li [arXiv]
    NeurIPS 2023.
  12. Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming
    Chunlin Sun, Shang Liu, Xiaocheng Li. [arXiv]
    ICML 2023.
  13. Neural Choice Model: Parameter Estimation and Assortment Optimization
    Hanzhao Wang, Zhongze Cai, Xiaocheng Li, Kalyan Talluri.
    Draft available soon.
  14. Stochastic Inverse Optimization
    John Birge, Xiaocheng Li, Chunlin Sun. [pdf]
  15. Deep Learning for Choice Modeling
    Zhongze Cai, Hanzhao Wang, Kalyan Talluri, Xiaocheng Li. [arXiv]
    Under review.
  16. Learning to Sell a Focal-ancillary Combination
    Hanzhao Wang, Xiaocheng Li, Kalyan Talluri. [arXiv]
    Under review.
  17. Learning from Stochatically Revealed Preference
    John Birge, Xiaocheng Li, Chunlin Sun. [arXiv]
    NeurIPS 2022.
  18. Non-stationary Bandits with Knapsacks
    Shang Liu, Jiashuo Jiang, Xiaocheng Li. [arXiv]
    NeurIPS 2022.
  19. On Dynamic Pricing with Covariates
    Hanzhao Wang, Kalyan Talluri, Xiaocheng Li
    Minor revision at Operations Research (technical note). [arXiv]
  20. Online Bin Packing with Known T
    Shang Liu, Xiaocheng Li
    Major revision at Mathematics of Operations Research. [arXiv]
  21. The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks
    Xiaocheng Li, Chunlin Sun, Yinyu Ye
    ICML 2021 (Long talk). [arXiv]
  22. An Improved Analysis of LP-based Control for Revenue Management
    Guanting Chen, Xiaocheng Li, Yinyu Ye
    Operations Research (Technical Note), 2022. [arXiv]
    Fairness aspect of the problem: [arXiv]
  23. Online Stochastic Optimization with Wasserstein Based Non-stationarity
    Jiashuo Jiang, Xiaocheng Li, Jiawei Zhang
    Management Science, 2024. [arXiv]
  24. Simple and Fast Algorithm for Binary Integer and Online Linear Programming
    Xiaocheng Li, Chunlin Sun, Yinyu Ye
    NeurIPS 2020 (Long version at Mathematical Programming). [arXiv]
  25. Online Linear Programming: Dual Convergence, New Algorithms, and Regret Bounds
    Xiaocheng Li, Yinyu Ye
    Operations Research, 2021. [arXiv]
  26. Dynamic Pricing with External Information and Limited Inventory
    Xiaocheng Li, Zeyu Zheng
    Management Science, 2023. [SSRN]
  27. Demand Prediction, Predictive Shipping, and Product Allocation for Large-scale E-commerce
    Xiaocheng Li, Yufeng Zheng, Zhenpeng Zhou, Zeyu Zheng [SSRN]
    M&SOM data-driven research challenge, finalist.
  28. Quantile Markov Decision Processes
    Xiaocheng Li, Huaiyang Zhong, Margaret L. Brandeau
    Operations Research, 2021 [arXiv]
  29. Data-Driven Ranking and Selection: High-Dimensional Covariates and General Dependence
    Xiaocheng Li, Xiaowei Zhang, Zeyu Zheng
    WSC 2018 [Link]
  30. Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans.
    Huaiyang Zhong, Xiaocheng Li, David Lobell, Stefano Ermon, Margaret L. Brandeau
    Environment Systems and Decisions, 38(4), 458-470, 2018 [Link]
  31. Recurrent Autoregressive Networks for Online Multi-Object Tracking
    Kuan Fang, Yu Xiang, Xiaocheng Li, Silvio Savarese
    WACV 2018. [Link]
  32. Optimizing Chemical Reactions with Deep Reinforcement Learning
    Zhenpeng Zhou, Xiaocheng Li, Richard N. Zare
    American Chemical Society (ACS) Central Science 2017, 3(12), 1337-1344. [Link]
  33. Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
    Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, Stefano Ermon
    Winner, Best Solution Prize for 2016 World Bank Big Data Innovation Challenge
    AAAI 2017. [Link]
  34. A Closed-form Expansion Approach for Pricing Discretely Monitored Variance Swaps
    Chenxu Li, Xiaocheng Li (Undergraduate thesis)
    Operations Research Letters, 2015. [Link]