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

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

• 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]