3.2k citations · 9.8k across the 51 of their papers we have counts for
9 papers · 1 filter
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures
Dan Hendrycks, Andy Zou, Mantas Mazeika +4
In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy. These other goals include out-of-…
Efficient and Local Parallel Random Walks
Michael Kapralov, Silvio Lattanzi, Navid Nouri +1
Random walks are a fundamental primitive used in many machine learning algorithms with several applications in clustering and semi-supervised learning. Despite their relevance, the…
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges
Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks +3
Machine learning models often encounter samples that are diverged from the training distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently assign…
What Would Jiminy Cricket Do? Towards Agents That Behave Morally
Dan Hendrycks, Mantas Mazeika, Andy Zou +6
When making everyday decisions, people are guided by their conscience, an internal sense of right and wrong. By contrast, artificial agents are currently not endowed with a moral s…
Unsolved Problems in ML Safety
Dan Hendrycks, Nicholas Carlini, John Schulman +1
Machine learning (ML) systems are rapidly increasing in size, are acquiring new capabilities, and are increasingly deployed in high-stakes settings. As with other powerful technolo…
VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data
Dina Bashkirova, Dan Hendrycks, Donghyun Kim +5
Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i.e., the same domain. This over-estimates future accuracy on o…