Dan Hendrycks

I am a second-year PhD student in computer science at UC Berkeley where I am advised by Dawn Song and Jacob Steinhardt. I am interested in Machine Ethics, Machine Learning for Cybersecurity, and Reliable Machine Learning. In 2018 I received my BS from UChicago. Last summer I interned at DeepMind. hendrycks ατ berkeley.edu Research Updates (Twitter)

Works

• Aligning AI With Shared Human Values
  Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt
  


• The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
  Dan Hendrycks, Steven Basart*, Norman Mu*, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, Justin Gilmer
  [arXiv] [code]


• Pretrained Transformers Improve Out-of-Distribution Robustness
  Dan Hendrycks*, Xiaoyuan Liu*, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, Dawn Song
  ACL 2020
  [arXiv] [code]


• AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
  Dan Hendrycks*, Norman Mu*, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan
  ICLR 2020
  [arXiv] [code]


• A Benchmark for Anomaly Segmentation
  Dan Hendrycks*, Steven Basart*, Mantas Mazeika, Mohammadreza Mostajabi, Jacob Steinhardt, Dawn Song
  [arXiv] [code]


• Natural Adversarial Examples
  Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, Dawn Song
  [arXiv] [code]


• Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
  Dan Hendrycks, Mantas Mazeika*, Saurav Kadavath*, Dawn Song
  NeurIPS 2019
  [arXiv] [code]


• Testing Robustness Against Unforeseen Adversaries
  Daniel Kang*, Yi Sun*, Dan Hendrycks, Tom Brown, Jacob Steinhardt
  [arXiv] [code]


• Adversarial Example Researchers Need to Expand What is Meant by ‘Robustness’
  Justin Gilmer, Dan Hendrycks
  Distill 2019
  


• Using Pre-Training Can Improve Model Robustness and Uncertainty
  Dan Hendrycks, Kimin Lee, Mantas Mazeika
  ICML 2019
  [arXiv] [code]


• Deep Anomaly Detection with Outlier Exposure
  Dan Hendrycks, Mantas Mazeika, Thomas Dietterich
  ICLR 2019
  [arXiv] [code]


• Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
  Dan Hendrycks, Thomas Dietterich
  ICLR 2019
  [arXiv] [code]


• Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
  Dan Hendrycks*, Mantas Mazeika*, Duncan Wilson, Kevin Gimpel
  NeurIPS 2018
  [arXiv] [code]


• Open Category Detection with PAC Guarantees
  Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, Dan Hendrycks
  ICML 2018
  [arXiv] [code]


• A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
  Dan Hendrycks, Kevin Gimpel
  ICLR 2017
  [arXiv] [code]


• Early Methods for Detecting Adversarial Images
  Dan Hendrycks, Kevin Gimpel
  ICLR 2017 Workshop
  [arXiv]


• Gaussian Error Linear Units (GELUs)
  Dan Hendrycks, Kevin Gimpel
  Technical Report, in which we propose the SiLU and the GELU, implemented in the PyTorch library and used in BERT and OpenAI GPT.
  [arXiv]

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