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2020 – today
- 2024
[j2]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:
Scalable watermarking for identifying large language model outputs. Nat. 634(8035): 818-823 (2024)- [c9]Dingli Yu, Simran Kaur, Arushi Gupta, Jonah Brown-Cohen, Anirudh Goyal, Sanjeev Arora:
SKILL-MIX: a Flexible and Expandable Family of Evaluations for AI Models. ICLR 2024 - [c8]Jonah Brown-Cohen, Geoffrey Irving, Georgios Piliouras:
Scalable AI Safety via Doubly-Efficient Debate. ICML 2024 - [i11]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:
On scalable oversight with weak LLMs judging strong LLMs. CoRR abs/2407.04622 (2024) - 2023
- [c7]Ezgi Korkmaz, Jonah Brown-Cohen:
Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions. ICML 2023: 17534-17543 - [i10]Ezgi Korkmaz, Jonah Brown-Cohen:
Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions. CoRR abs/2306.05873 (2023) - [i9]Dingli Yu, Simran Kaur, Arushi Gupta, Jonah Brown-Cohen, Anirudh Goyal, Sanjeev Arora:
Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models. CoRR abs/2310.17567 (2023) - [i8]Jonah Brown-Cohen, Geoffrey Irving, Georgios Piliouras:
Scalable AI Safety via Doubly-Efficient Debate. CoRR abs/2311.14125 (2023) - 2021
- [c6]Jonah Brown-Cohen:
Faster Algorithms and Constant Lower Bounds for the Worst-Case Expected Error. NeurIPS 2021: 27709-27719 - [c5]Per Austrin, Jonah Brown-Cohen, Johan Håstad:
Optimal Inapproximability with Universal Factor Graphs. SODA 2021: 434-453 - [i7]Jonah Brown-Cohen:
Faster Algorithms and Constant Lower Bounds for the Worst-Case Expected Error. CoRR abs/2112.13832 (2021) - 2020
- [c4]Jonah Brown-Cohen, Prasad Raghavendra:
Extended Formulation Lower Bounds for Refuting Random CSPs. SODA 2020: 305-324
2010 – 2019
- 2019
- [c3]Jonah Brown-Cohen, Arvind Narayanan, Alexandros Psomas
, S. Matthew Weinberg:
Formal Barriers to Longest-Chain Proof-of-Stake Protocols. EC 2019: 459-473 - [i6]Jonah Brown-Cohen, Prasad Raghavendra:
Extended Formulation Lower Bounds for Refuting Random CSPs. CoRR abs/1911.02911 (2019) - [i5]Per Austrin, Jonah Brown-Cohen, Johan Håstad:
Optimal Inapproximability with Universal Factor Graphs. Electron. Colloquium Comput. Complex. TR19 (2019) - 2018
- [b1]Jonah Brown-Cohen:
Extended Formulation Lower Bounds for Combinatorial Optimization. University of California, Berkeley, USA, 2018 - [i4]Jonah Brown-Cohen, Arvind Narayanan, Christos-Alexandros Psomas, S. Matthew Weinberg:
Formal Barriers to Longest-Chain Proof-of-Stake Protocols. CoRR abs/1809.06528 (2018) - 2017
- [j1]Gábor Braun, Jonah Brown-Cohen, Arefin Huq, Sebastian Pokutta, Prasad Raghavendra, Aurko Roy, Benjamin Weitz, Daniel Zink:
The matching problem has no small symmetric SDP. Math. Program. 165(2): 643-662 (2017) - 2016
- [c2]Jonah Brown-Cohen, Prasad Raghavendra:
Correlation Decay and Tractability of CSPs. ICALP 2016: 79:1-79:13 - [c1]Gábor Braun, Jonah Brown-Cohen, Arefin Huq, Sebastian Pokutta, Prasad Raghavendra, Aurko Roy, Benjamin Weitz, Daniel Zink:
The matching problem has no small symmetric SDP. SODA 2016: 1067-1078 - 2015
- [i3]Jonah Brown-Cohen, Prasad Raghavendra:
Combinatorial Optimization Algorithms via Polymorphisms. CoRR abs/1501.01598 (2015) - [i2]Gábor Braun, Jonah Brown-Cohen, Arefin Huq, Sebastian Pokutta, Prasad Raghavendra, Aurko Roy, Benjamin Weitz, Daniel Zink:
The matching problem has no small symmetric SDP. CoRR abs/1504.00703 (2015) - [i1]Jonah Brown-Cohen, Prasad Raghavendra:
Combinatorial Optimization Algorithms via Polymorphisms. Electron. Colloquium Comput. Complex. TR15 (2015)
Coauthor Index
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