Erik Jenner - RankMe

23 papers A* 6Journal 17

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Frontier Models Can Take Actions at Low Probabilities. CoRR Alex Serrano, Wen Xing, David Lindner, Erik Jenner
2025	J	`jnl`	Can Reasoning Models Obfuscate Reasoning? Stress-Testing Chain-of-Thought Monitorability. CoRR Artur Zolkowski, Wen Xing, David Lindner, Florian Tramèr, Erik Jenner
2025	J	`jnl`	Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety. CoRR Tomek Korbak, Mikita Balesni, Elizabeth Barnes, Yoshua Bengio, Joe Benton, Joseph Bloom, Mark Chen, Alan Cooney, Allan Dafoe, Anca D. Dragan, Scott Emmons, Owain Evans, David Farhi, Ryan Greenblatt, Dan Hendrycks, Marius Hobbhahn, Evan Hubinger, Geoffrey Irving, Erik Jenner, Daniel Kokotajlo, Victoria Krakovna, Shane Legg, David Lindner, David Luan, Aleksander Madry, Julian Michael, Neel Nanda, Dave Orr, Jakub Pachocki, Ethan Perez, Mary Phuong, Fabien Roger, Joshua Saxe, Buck Shlegeris, Martín Soto, Eric Steinberger, Jasmine Wang, Wojciech Zaremba, Bowen Baker, Rohin Shah, Vladimir Mikulik
2025	A*	`conf`	Diffusion On Syntax Trees For Program Synthesis. ICLR Shreyas Kapur, Erik Jenner, Stuart Russell
2025	J	`jnl`	RL-Obfuscation: Can Language Models Learn to Evade Latent-Space Monitors? CoRR Rohan Gupta, Erik Jenner
2025	J	`jnl`	When Chain of Thought is Necessary, Language Models Struggle to Evade Monitors. CoRR Scott Emmons, Erik Jenner, David K. Elson, Rif A. Saurous, Senthooran Rajamanoharan, Heng Chen, Irhum Shafkat, Rohin Shah
2024	J	`jnl`	Diffusion On Syntax Trees For Program Synthesis. CoRR Shreyas Kapur, Erik Jenner, Stuart Russell
2024	A*	`conf`	Evidence of Learned Look-Ahead in a Chess-Playing Neural Network. NeurIPS Erik Jenner, Shreyas Kapur, Vasil Georgiev, Cameron Allen, Scott Emmons, Stuart J. Russell
2024	J	`jnl`	Evidence of Learned Look-Ahead in a Chess-Playing Neural Network. CoRR Erik Jenner, Shreyas Kapur, Vasil Georgiev, Cameron Allen, Scott Emmons, Stuart Russell
2024	J	`jnl`	Foundational Challenges in Assuring Alignment and Safety of Large Language Models. CoRR Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, José Hernández-Orallo, Lewis Hammond, Eric J. Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi C. Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Yoshua Bengio, Danqi Chen, Samuel Albanie, Tegan Maharaj, Jakob N. Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger
2024	J	`jnl`	Foundational Challenges in Assuring Alignment and Safety of Large Language Models. Trans. Mach. Learn. Res. Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, José Hernández-Orallo, Lewis Hammond, Eric J. Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Chenyu Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Aleksandar Petrov, Christian Schröder de Witt, Sumeet Ramesh Motwani, Yoshua Bengio, Danqi Chen, Philip Torr, Samuel Albanie, Tegan Maharaj, Jakob Nicolaus Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger
2024	J	`jnl`	Obfuscated Activations Bypass LLM Latent-Space Defenses. CoRR Luke Bailey, Alex Serrano, Abhay Sheshadri, Mikhail Seleznyov, Jordan Taylor, Erik Jenner, Jacob Hilton, Stephen Casper, Carlos Guestrin, Scott Emmons
2024	A*	`conf`	STARC: A General Framework For Quantifying Differences Between Reward Functions. ICLR Joar Max Viktor Skalse, Lucy Farnik, Sumeet Ramesh Motwani, Erik Jenner, Adam Gleave, Alessandro Abate
2024	A*	`conf`	When Your AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback. NeurIPS Leon Lang, Davis Foote, Stuart J. Russell, Anca D. Dragan, Erik Jenner, Scott Emmons
2024	J	`jnl`	When Your AIs Deceive You: Challenges with Partial Observability of Human Evaluators in Reward Learning. CoRR Leon Lang, Davis Foote, Stuart Russell, Anca D. Dragan, Erik Jenner, Scott Emmons
2023	J	`jnl`	STARC: A General Framework For Quantifying Differences Between Reward Functions. CoRR Joar Skalse, Lucy Farnik, Sumeet Ramesh Motwani, Erik Jenner, Adam Gleave, Alessandro Abate
2022	J	`jnl`	Calculus on MDPs: Potential Shaping as a Gradient. CoRR Erik Jenner, Herke van Hoof, Adam Gleave
2022	J	`jnl`	Preprocessing Reward Functions for Interpretability. CoRR Erik Jenner, Adam Gleave
2022	A*	`conf`	Steerable Partial Differential Operators for Equivariant Neural Networks. ICLR Erik Jenner, Maurice Weiler
2022	J	`jnl`	imitation: Clean Imitation Learning Implementations. CoRR Adam Gleave, Mohammad Taufeeque, Juan Rocamonde, Erik Jenner, Steven H. Wang, Sam Toyer, Maximilian Ernestus, Nora Belrose, Scott Emmons, Stuart Russell
2021	A*	`conf`	Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice. ICCV Erik Jenner, Enrique Fita Sanmartín, Fred A. Hamprecht
2021	J	`jnl`	Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice. CoRR Erik Jenner, Enrique Fita Sanmartín, Fred A. Hamprecht
2021	J	`jnl`	Steerable Partial Differential Operators for Equivariant Neural Networks. CoRR Erik Jenner, Maurice Weiler