Research

• Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks

  Curtis Northcutt, Anish Athalye, and Jonas Mueller

  35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks

  Demo, Code, Blog Post, Press [1, 2, 3, 4, 5, 6, 7, 8]

• Confident Learning: Estimating Uncertainty in Dataset Labels

  Curtis Northcutt, Lu Jiang, and Isaac Chuang

  Journal of Artificial Intelligence Research (JAIR), Vol. 70 (2021)

  Code, Blog Post

• Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels

  Curtis Northcutt, Tailin Wu, and Isaac Chuang

  33rd Conference on Uncertainty in Artificial Intelligence (UAI 2017)

• Back to the Basics: Revisiting Out-of-Distribution Detection Baselines

  Johnson Kuan and Jonas Mueller

  ICML Workshop on Principles of Distribution Shift, 2022

  Code

• Model-Agnostic Label Quality Scoring to Detect Real-World Label Errors

  Johnson Kuan and Jonas Mueller

  ICML DataPerf Workshop, 2022

  Code

• EgoCom: A Multi-person Multi-modal Egocentric Communications Dataset

  Curtis Northcutt, Zha Shengxin, Steven Lovegrove, and Richard Newcombe

  IEEE Transactions on Pattern Analysis and Machine Intelligence

  Code