Risks from Learned Optimization in Advanced Machine Learning Systems. - Evan Hubinger; Chris van Merwijk; Vladimir Mikulik; Joar Skalse; Scott Garrabrant (CoRR, 2019)

Neural networks are a priori biased towards Boolean functions with low entropy. - Chris Mingard; Joar Skalse; Guillermo Valle Pérez; David Martínez-Rubio; Vladimir Mikulik; Ard A. Louis (CoRR, 2019)

Safety Properties of Inductive Logic Programming. - Gavin Leech; Nandi Schoots; Joar Skalse (SafeAI@AAAI, 2021)

Reinforcement Learning in Newcomblike Environments. - James Bell 001; Linda Linsefors; Caspar Oesterheld; Joar Skalse (NeurIPS, 2021)

A General Counterexample to Any Decision Theory and Some Responses. - Joar Skalse (CoRR, 2021)

Is SGD a Bayesian sampler? Well, almost. - Chris Mingard; Guillermo Valle Pérez; Joar Skalse; Ard A. Louis (J. Mach. Learn. Res., 2021)

Lexicographic Multi-Objective Reinforcement Learning. - Joar Skalse; Lewis Hammond; Charlie Griffin; Alessandro Abate (IJCAI, 2022)

Defining and Characterizing Reward Gaming. - Joar Skalse; Nikolaus H. R. Howe; Dmitrii Krasheninnikov; David Krueger 001 (NeurIPS, 2022)

Defining and Characterizing Reward Hacking. - Joar Skalse; Nikolaus H. R. Howe; Dmitrii Krasheninnikov; David Krueger 001 (CoRR, 2022)

Misspecification in Inverse Reinforcement Learning. - Joar Skalse; Alessandro Abate (AAAI, 2023)

Invariance in Policy Optimisation and Partial Identifiability in Reward Learning. - Joar Max Viktor Skalse; Matthew Farrugia-Roberts; Stuart Russell 001; Alessandro Abate; Adam Gleave (ICML, 2023)

On the limitations of Markovian rewards to express multi-objective, risk-sensitive, and modal tasks. - Joar Skalse; Alessandro Abate (UAI, 2023)

Goodhart's Law in Reinforcement Learning. - Jacek Karwowski; Oliver Hayman; Xingjian Bai; Klaus Kiendlhofer; Charlie Griffin; Joar Max Viktor Skalse (ICLR, 2024)

Quantifying the Sensitivity of Inverse Reinforcement Learning to Misspecification. - Joar Max Viktor Skalse; Alessandro Abate (ICLR, 2024)

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)

On the Expressivity of Objective-Specification Formalisms in Reinforcement Learning. - Rohan Subramani; Marcus Williams; Max Heitmann; Halfdan Holm; Charlie Griffin; Joar Max Viktor Skalse (ICLR, 2024)

Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems. - David Dalrymple; Joar Skalse; Yoshua Bengio; Stuart Russell 001; Max Tegmark; Sanjit Seshia; Steve Omohundro; Christian Szegedy; Ben Goldhaber; Nora Ammann; Alessandro Abate; Joe Halpern; et al. (CoRR, 2024)

The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret. - Lukas Fluri; Leon Lang; Alessandro Abate; Patrick Forré; David Krueger 001; Joar Skalse (CoRR, 2024)

Partial Identifiability and Misspecification in Inverse Reinforcement Learning. - Joar Skalse; Alessandro Abate (CoRR, 2024)

Partial Identifiability in Inverse Reinforcement Learning for Agents with Non-Exponential Discounting. - Joar Max Viktor Skalse; Alessandro Abate (AAAI, 2025)