Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice. - Erik Jenner; Enrique Fita Sanmartín; Fred A. Hamprecht (ICCV, 2021)

Steerable Partial Differential Operators for Equivariant Neural Networks. - Erik Jenner; Maurice Weiler (ICLR, 2022)

Preprocessing Reward Functions for Interpretability. - Erik Jenner; Adam Gleave (CoRR, 2022)

Calculus on MDPs: Potential Shaping as a Gradient. - Erik Jenner; Herke van Hoof; Adam Gleave (CoRR, 2022)

imitation: Clean Imitation Learning Implementations. - Adam Gleave; Mohammad Taufeeque; Juan Rocamonde; Erik Jenner; Steven H. Wang; Sam Toyer; Maximilian Ernestus; Nora Belrose; Scott Emmons; Stuart Russell 001 (CoRR, 2022)

STARC: A General Framework For Quantifying Differences Between Reward Functions. - Joar Max Viktor Skalse; Lucy Farnik; Sumeet Ramesh Motwani; Erik Jenner; Adam Gleave; Alessandro Abate (ICLR, 2024)

Evidence of Learned Look-Ahead in a Chess-Playing Neural Network. - Erik Jenner; Shreyas Kapur; Vasil Georgiev; Cameron Allen; Scott Emmons; Stuart J. Russell (NeurIPS, 2024)

When Your AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback. - Leon Lang; Davis Foote; Stuart J. Russell; Anca D. Dragan; Erik Jenner; Scott Emmons (NeurIPS, 2024)

When Your AIs Deceive You: Challenges with Partial Observability of Human Evaluators in Reward Learning. - Leon Lang; Davis Foote; Stuart Russell 001; Anca D. Dragan; Erik Jenner; Scott Emmons (CoRR, 2024)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models. - 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; et al. (CoRR, 2024)

Obfuscated Activations Bypass LLM Latent-Space Defenses. - Luke Bailey; Alex Serrano; Abhay Sheshadri; Mikhail Seleznyov; Jordan Taylor; Erik Jenner; Jacob Hilton; Stephen Casper; Carlos Guestrin; Scott Emmons (CoRR, 2024)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models. - 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; et al. (Trans. Mach. Learn. Res., 2024)

Diffusion On Syntax Trees For Program Synthesis. - Shreyas Kapur; Erik Jenner; Stuart Russell 001 (ICLR, 2025)

RL-Obfuscation: Can Language Models Learn to Evade Latent-Space Monitors? - Rohan Gupta; Erik Jenner (CoRR, 2025)

When Chain of Thought is Necessary, Language Models Struggle to Evade Monitors. - Scott Emmons; Erik Jenner; David K. Elson; Rif A. Saurous; Senthooran Rajamanoharan; Heng Chen; Irhum Shafkat; Rohin Shah (CoRR, 2025)

Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety. - Tomek Korbak; Mikita Balesni; Elizabeth Barnes; Yoshua Bengio; Joe Benton; Joseph Bloom; Mark Chen 003; Alan Cooney; Allan Dafoe; Anca D. Dragan; Scott Emmons; Owain Evans; et al. (CoRR, 2025)