10 citations · 25 across the 4 of their papers we have counts for
4 papers
Adversarial Training for High-Stakes Reliability
Daniel M. Ziegler, Seraphina Nix, Lawrence Chan +9
In the future, powerful AI systems may be deployed in high-stakes settings, where a single failure could be catastrophic. One technique for improving AI safety in high-stakes setti…
On the Detection of Mixture Distributions with applications to the Most Biased Coin Problem
Kevin Jamieson, Daniel Haas, Ben Recht
This paper studies the trade-off between two different kinds of pure exploration: breadth versus depth. The most biased coin problem asks how many total coin flips are required to…
CLAMShell: Speeding up Crowds for Low-latency Data Labeling
Daniel Haas, Jiannan Wang, Eugene Wu +1
Data labeling is a necessary but often slow process that impedes the development of interactive systems for modern data analysis. Despite rising demand for manual data labeling, th…
Detecting People in Cubist Art
Shiry Ginosar, Daniel Haas, Timothy Brown +1
Although the human visual system is surprisingly robust to extreme distortion when recognizing objects, most evaluations of computer object detection methods focus only on robustne…