Publications

Preprints
  1. Multi-level Monte-Carlo Gradient Methods for Stochastic Optimization with Biased Oracles
    Yifan Hu, Jie Wang, Xin Chen, Niao He.

  2. Regularization for Adversarial Robust Learning [Slides]
    Jie Wang, Rui Gao, Yao Xie. (Winner of the 18th INFORMS DMDA Workshop Best Paper Competition Award, 2 out of 57)

  3. Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances
    Jie Wang, March Boedihardjo, Yao Xie. (Runner-up of the INFORMS 2024 Data Mining Best Paper Award Competition)

  4. Variable Selection for Kernel Two-Sample Tests
    Jie Wang, Santanu Dey, Yao Xie.
    (Selected for Poster Presentation at Mixed Integer Programming (MIP) Workshop 2023)
    (Runner-up of the INFORMS 2024 Computing Society (ICS) Student Paper Award)

  5. Training By Vanilla SGD with Larger Learning Rates
    Yueyao Yu, Jie Wang, Wenye Li, Yin Zhang.

Conference Proceedings
  1. Sparse Degree Optimization for BATS Codes
    Hoover H. F. Yin, Jie Wang. 2024 IEEE Information Theory Workshop

  2. Distributionally Robust Degree Optimization for BATS Codes
    Hoover H. F. Yin, Jie Wang, Sherman S. M. Chow. 2024 IEEE International Symposium on Information Theory

  3. Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets
    Jie Wang, Rui Gao, Yao Xie. 2024 IEEE International Symposium on Information Theory

  4. Conditional Stochastic Bilevel Optimization
    Yifan Hu, Jie Wang, Yao Xie, Andreas Krause, Daniel Kuhn. NeurIPS 2023 (Journal version to be submitted to Operations Research)

  5. Reliable Adaptive Recoding for Batched Network Coding with Burst-Noise Channels
    Jie Wang, Talha Bozkus, Yao Xie, Urbashi Mitra. Asilomar 2023

  6. Improving Sepsis Prediction Model Generalization With Optimal Transport
    Jie Wang, Ronald Moore, Rishikesan Kamaleswaran, Yao Xie. 2022 Machine Learning for Health (ML4H)

  7. A Data-Driven Approach to Robust Hypothesis Testing Using Sinkhorn Uncertainty Sets
    Jie Wang, Yao Xie. 2022 IEEE International Symposium on Information Theory (ISIT)

  8. Two-sample Test with Kernel Projected Wasserstein Distance
    Jie Wang, Rui Gao, Yao Xie. 2022 Artificial Intelligence and Statistics (AISTATS) (Oral Presentation! Rate: 44/1685=0.026)

  9. Two-sample Test using Projected Wasserstein Distance
    Jie Wang, Rui Gao, Yao Xie. 2021 IEEE International Symposium on Information Theory (ISIT)

  10. Small-Sample Inferred Adaptive Recoding for Batched Network Coding
    Jie Wang, Zhiyuan Jia, Hoover H. F. Yin, Shenghao Yang. 2021 IEEE International Symposium on Information Theory (ISIT)

  11. Upper Bound Scalability on Achievable Rates of Batched Codes for Line Networks
    Shenghao Yang, Jie Wang. 2020 IEEE International Symposium on Information Theory (ISIT)

  12. On the Capacity Scalability of Line Networks with Buffer Size Constraints
    Shenghao Yang, Jie Wang, Yanyan Dong, Yiheng Zhang. 2019 IEEE International Symposium on Information Theory (ISIT)

  13. On the Tightness of a Cut-Set Bound on Network Function Computation
    Jie Wang, Shenghao Yang, Congduan Li. 2018 IEEE International Symposium on Information Theory (ISIT)

  14. Efficient Underwater Sensor Network Data Collection Employing Unmanned Ships
    Jie Wang, Jun Ma, Jianyu Yang, Shenghao Yang. The 14th International Conference on Underwater Networks & Systems (WUWNet’19), At Atlanta, GA, USA (Extended Manuscript)

Technical Note
  1. Deep Learning for TikTok Video Popularity Prediction: An Variational Inference Perspective
    Jie Wang and Yongchun Li, Winner of the INFORMS Data Mining Society's Data Competition in 2024

  2. Reliable Offline Pricing in eCommerce Decision-Making: A Distributionally Robust Viewpoint
    Jie Wang. Finalist presentation for the INFORMS Data Mining Society's Data Competition in 2023

  3. Finite-length Code and Application in Network Coding
    Shenghao Yang, Yanyan Dong, Jie Wang. IEEE INFORMATION THEORY SOCIETY GUANGZHOU CHAPTER NEWSLETTER, No.1, July 2020