NIPS Proceedingsβ

Ludwig Schmidt

9 Papers

  • A Meta-Analysis of Overfitting in Machine Learning (2019)
  • Model Similarity Mitigates Test Set Overuse (2019)
  • Unlabeled Data Improves Adversarial Robustness (2019)
  • Adversarially Robust Generalization Requires More Data (2018)
  • Communication-Efficient Distributed Learning of Discrete Distributions (2017)
  • On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks (2017)
  • Fast recovery from a union of subspaces (2016)
  • Differentially Private Learning of Structured Discrete Distributions (2015)
  • Practical and Optimal LSH for Angular Distance (2015)