Jonah Brown-Cohen

According to our database, Jonah Brown-Cohen authored at least 15 papers between 2015 and 2025.

Collaborative distances:

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

2025

Avoiding Obfuscation with Prover-Estimator Debate.

Jonah Brown-Cohen, Geoffrey Irving, Georgios Piliouras

CoRR, June, 2025

An Approach to Technical AGI Safety and Security.

Rohin Shah, Alex Irpan, Alexander Matt Turner, Anna Wang, Arthur Conmy, David Lindner, Jonah Brown-Cohen, Lewis Ho, Neel Nanda, Raluca Ada Popa, Rishub Jain, Rory Greig, Samuel Albanie, Scott Emmons, Sebastian Farquhar, Sébastien Krier, Senthooran Rajamanoharan, Sophie Bridgers, Tobi Ijitoye, Tom Everitt, Victoria Krakovna, Vikrant Varma, Vladimir Mikulik, Zachary Kenton, Dave Orr, Shane Legg, Noah D. Goodman, Allan Dafoe, Four Flynn, Anca D. Dragan

CoRR, April, 2025

2024

Scalable watermarking for identifying large language model outputs.

Sumanth Dathathri, Abigail See, Sumedh Ghaisas, Po-Sen Huang, Rob McAdam, Johannes Welbl, Vandana Bachani, Alex Kaskasoli, Robert Stanforth, Tatiana Matejovicova, Jamie Hayes, Nidhi Vyas, Majd Al Merey, Jonah Brown-Cohen, Rudy Bunel, Borja Balle, A. Taylan Cemgil, Zahra Ahmed, Kitty Stacpoole, Ilia Shumailov, Ciprian Baetu, Sven Gowal, Demis Hassabis, Pushmeet Kohli

Nat., October, 2024

On scalable oversight with weak LLMs judging strong LLMs.

Zachary Kenton, Noah Y. Siegel, János Kramár, Jonah Brown-Cohen, Samuel Albanie, Jannis Bulian, Rishabh Agarwal, David Lindner, Yunhao Tang, Noah D. Goodman, Rohin Shah

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Scalable AI Safety via Doubly-Efficient Debate.

Jonah Brown-Cohen, Geoffrey Irving, Georgios Piliouras

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

SKILL-MIX: a Flexible and Expandable Family of Evaluations for AI Models.

Dingli Yu, Simran Kaur, Arushi Gupta, Jonah Brown-Cohen, Anirudh Goyal, Sanjeev Arora

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions.

Ezgi Korkmaz, Jonah Brown-Cohen

Proceedings of the International Conference on Machine Learning, 2023

2021

Faster Algorithms and Constant Lower Bounds for the Worst-Case Expected Error.

Jonah Brown-Cohen

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Extended Formulation Lower Bounds for Refuting Random CSPs.

Jonah Brown-Cohen, Prasad Raghavendra

Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, 2020

2019

Optimal Inapproximability with Universal Factor Graphs.

Per Austrin, Jonah Brown-Cohen, Johan Håstad

Electron. Colloquium Comput. Complex., 2019

Formal Barriers to Longest-Chain Proof-of-Stake Protocols.

Jonah Brown-Cohen, Arvind Narayanan, Alexandros Psomas, S. Matthew Weinberg

Proceedings of the 2019 ACM Conference on Economics and Computation, 2019

2018

Extended Formulation Lower Bounds for Combinatorial Optimization.

Jonah Brown-Cohen

PhD thesis, 2018

2017

The matching problem has no small symmetric SDP.

Gábor Braun, Jonah Brown-Cohen, Arefin Huq, Sebastian Pokutta, Prasad Raghavendra, Aurko Roy, Benjamin Weitz, Daniel Zink

Math. Program., 2017

2016

Correlation Decay and Tractability of CSPs.

Jonah Brown-Cohen, Prasad Raghavendra

Proceedings of the 43rd International Colloquium on Automata, Languages, and Programming, 2016

2015

Combinatorial Optimization Algorithms via Polymorphisms.

Jonah Brown-Cohen, Prasad Raghavendra

Electron. Colloquium Comput. Complex., 2015