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Jacob Steinhardt

Researcher at University of California, Berkeley

Publications -  116
Citations -  8821

Jacob Steinhardt is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Robustness (computer science). The author has an hindex of 28, co-authored 93 publications receiving 5444 citations. Previous affiliations of Jacob Steinhardt include Stanford University & Massachusetts Institute of Technology.

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Concrete Problems in AI Safety

TL;DR: A list of five practical research problems related to accident risk, categorized according to whether the problem originates from having the wrong objective function, an objective function that is too expensive to evaluate frequently, or undesirable behavior during the learning process, are presented.
Proceedings Article

Certified Defenses against Adversarial Examples

TL;DR: This work proposes a method based on a semidefinite relaxation that outputs a certificate that for a given network and test input, no attack can force the error to exceed a certain value, providing an adaptive regularizer that encourages robustness against all attacks.
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The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization

TL;DR: It is found that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work.
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Natural Adversarial Examples

TL;DR: This work introduces two challenging datasets that reliably cause machine learning model performance to substantially degrade and curates an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out- of-dist distribution detection dataset created for ImageNet models.