Publications

2023

  1. Should We Learn Most Likely Functions or Parameters?
    Shikai Qiu, Tim GJ Rudner, Sanyam Kapoor, and Andrew Gordon Wilson
    Advances in Neural Information Processing Systems 2023
  2. Large Language Models Are Zero Shot Time Series Forecasters
    Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew Gordon Wilson
    Advances in Neural Information Processing Systems 2023
  3. Simple and Fast Group Robustness by Automatic Feature Reweighting
    Shikai Qiu, Andres Potapczynski, Pavel Izmailov, and Andrew Gordon Wilson
    International Conference on Machine Learning (ICML) 2023
  4. Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models
    Shikai Qiu, Shuo Han, Xiangyang Ju, Benjamin Nachman, and Haichen Wang
    The European Physical Journal C 2023
  5. Holistic approach to predicting top quark kinematic properties with the covariant particle transformer
    Shikai Qiu, Shuo Han, Xiangyang Ju, Benjamin Nachman, and Haichen Wang
    Physical Review D 2023
  6. Model-independent search for the presence of new physics in events including \(H→γγ\> \)with \(\sqrt s\) = 13 TeV pp data recorded by the ATLAS detector at the LHC
    ATLAS Collaboration, and  others
    Journal of High Energy Physics 2023

2024

  1. Compute Better Spent: Replacing Dense Layers with Structured Matrices
    International Conference on Machine Learning (ICML) 2024