Collin Burns

According to our database, Collin Burns authored at least 11 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision.

Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, Ilya Sutskever, Jeffrey Wu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Discovering Latent Knowledge in Language Models Without Supervision.

Collin Burns, Haotian Ye, Dan Klein, Jacob Steinhardt

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2021

Measuring Coding Challenge Competence With APPS.

Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, Jacob Steinhardt

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Measuring Mathematical Problem Solving With the MATH Dataset.

Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, Jacob Steinhardt

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review.

Dan Hendrycks, Collin Burns, Anya Chen, Spencer Ball

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Measuring Massive Multitask Language Understanding.

Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt

Proceedings of the 9th International Conference on Learning Representations, 2021

Aligning AI With Shared Human Values.

Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt

Proceedings of the 9th International Conference on Learning Representations, 2021

Limitations of Post-Hoc Feature Alignment for Robustness.

Collin Burns, Jacob Steinhardt

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

Interpreting Black Box Models via Hypothesis Testing.

Collin Burns, Jesse Thomason, Wesley Tansey

Proceedings of the FODS '20: ACM-IMS Foundations of Data Science Conference, 2020

Streaming Complexity of SVMs.

Alexandr Andoni, Collin Burns, Yi Li, Sepideh Mahabadi, David P. Woodruff

Proceedings of the Approximation, 2020

2019

Interpreting Black Box Models with Statistical Guarantees.

Collin Burns, Jesse Thomason, Wesley Tansey

CoRR, 2019