Research

Authors indicated by a (*) indicate equal contribution listed in alphabetical order, as is customary in theoretical computer science.

Preprints

2024

    Conference and journal publications

    2024

    1. Locally Stationary Distributions: A Framework for Analyzing Slow-Mixing Markov Chains
      IEEE Annual Symposium on Foundations of Computer Science, 2024
    2. Fast Mixing in Sparse Random Ising Models
      Kuikui Liu*Sidhanth Mohanty*Amit Rajaraman*, and David X Wu*
      IEEE Annual Symposium on Foundations of Computer Science, 2024
    3. Robust recovery for stochastic block models, simplified and generalized
      Sidhanth Mohanty*Prasad Raghavendra*, and David X Wu*
      ACM Symposium on Theory of Computing, 2024

    2023

    1. Precise Asymptotic Generalization for Multiclass Classification with Overparameterized Linear Models
      David X Wu*, and Anant Sahai
      Neural Information Processing Systems, 2023
    2. On the Training Instability of Shuffling SGD with Batch Normalization
      David X Wu*Chulhee Yun, and Suvrit Sra
      International Conference on Machine Learning, 2023
    3. Lower Bounds for Multiclass Classification with Overparameterized Linear Models
      David X Wu*, and Anant Sahai
      International Symposium on Information Theory, 2023

    2022

    1. Maximum A Posteriori Inference of Random Dot Product Graphs via Conic Programming
      David X Wu*David Palmer, and Daryl R DeFord
      SIAM Journal on Optimization, 2022