• Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients
    Aashiq Muhamed, Oscar Li, David Woodruff, Mona Diab, Virginia Smith
    [arxiv] 2024

  • OmniPred: Language Models as Universal Regressors
    Xingyou Song*, Oscar Li*, Chansoo Lee, Bangding Yang, Daiyi Peng, Sagi Perel, Yutian Chen
    [arxiv] 2024

  • Noise-Reuse in Online Evolution Strategies
    Oscar Li, James Harrison, Jascha Sohl-Dickstein, Virigina Smith, Luke Metz
    NeurIPS 2023
    [arxiv][video][poster]

  • Label Leakage and Protection in Two-party Split Learning
    Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, Chong Wang
    ICLR 2022
    [OpenReview][video]

  • Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution
    Oscar Li*, Amrith Setlur*, Virginia Smith
    NeurIPS 2021
    [arxiv][video]

  • Is Support Set Diversity Necessary for Meta-Learning?
    Amrith Setlur*, Oscar Li*, Virginia Smith
    Neurips 2020 MetaLearn Workshop
    [arxiv][poster]

  • This Looks Like That: Deep Learning for Interpretable Image Recognition
    Chaofan Chen*, Oscar Li*, Daniel Tao, Alina Barnett, Cynthia Rudin, Jonathan Su
    NeurIPS 2019 (spolight, top 3% papers)
    [arxiv][talk (05:12)]

  • Interpretable Image Recognition with Hierarchical Prototypes
    Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin
    AAAI HCOMP 2019
    [arxiv]

  • Deep Learning for Case-based Reasoning through prototypes: A Neural Network that explains its predictions
    Oscar Li*, Hao Liu*, Chaofan Chen, Cynthia Rudin
    AAAI 2018
    [arxiv]