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2020 – today
- 2024
[c11]Jacek Karwowski, Oliver Hayman, Xingjian Bai, Klaus Kiendlhofer, Charlie Griffin, Joar Max Viktor Skalse:
Goodhart's Law in Reinforcement Learning. ICLR 2024- [c10]Joar Max Viktor Skalse, Alessandro Abate:
Quantifying the Sensitivity of Inverse Reinforcement Learning to Misspecification. ICLR 2024 - [c9]Joar Max Viktor Skalse, Lucy Farnik, Sumeet Ramesh Motwani, Erik Jenner, Adam Gleave, Alessandro Abate:
STARC: A General Framework For Quantifying Differences Between Reward Functions. ICLR 2024 - [c8]Rohan Subramani, Marcus Williams, Max Heitmann, Halfdan Holm, Charlie Griffin, Joar Max Viktor Skalse:
On the Expressivity of Objective-Specification Formalisms in Reinforcement Learning. ICLR 2024 - [i15]Joar Skalse, Alessandro Abate:
On the Limitations of Markovian Rewards to Express Multi-Objective, Risk-Sensitive, and Modal Tasks. CoRR abs/2401.14811 (2024) - [i14]Joar Skalse, Alessandro Abate:
Quantifying the Sensitivity of Inverse Reinforcement Learning to Misspecification. CoRR abs/2403.06854 (2024) - [i13]David Dalrymple, Joar Skalse, Yoshua Bengio, Stuart Russell, Max Tegmark, Sanjit Seshia, Steve Omohundro, Christian Szegedy, Ben Goldhaber, Nora Ammann, Alessandro Abate, Joe Halpern, Clark W. Barrett, Ding Zhao, Tan Zhi-Xuan, Jeannette Wing, Joshua B. Tenenbaum:
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems. CoRR abs/2405.06624 (2024) - [i12]Lukas Fluri, Leon Lang, Alessandro Abate, Patrick Forré, David Krueger, Joar Skalse:
The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret. CoRR abs/2406.15753 (2024) - 2023
- [c7]Joar Skalse, Alessandro Abate:
Misspecification in Inverse Reinforcement Learning. AAAI 2023: 15136-15143 - [c6]Joar Max Viktor Skalse, Matthew Farrugia-Roberts, Stuart Russell, Alessandro Abate, Adam Gleave:
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning. ICML 2023: 32033-32058 - [c5]Joar Skalse, Alessandro Abate:
On the limitations of Markovian rewards to express multi-objective, risk-sensitive, and modal tasks. UAI 2023: 1974-1984 - [i11]Joar Skalse, Lucy Farnik, Sumeet Ramesh Motwani, Erik Jenner, Adam Gleave, Alessandro Abate:
STARC: A General Framework For Quantifying Differences Between Reward Functions. CoRR abs/2309.15257 (2023) - [i10]Jacek Karwowski, Oliver Hayman, Xingjian Bai, Klaus Kiendlhofer, Charlie Griffin, Joar Skalse:
Goodhart's Law in Reinforcement Learning. CoRR abs/2310.09144 (2023) - [i9]Rohan Subramani, Marcus Williams, Max Heitmann, Halfdan Holm, Charlie Griffin, Joar Skalse:
On The Expressivity of Objective-Specification Formalisms in Reinforcement Learning. CoRR abs/2310.11840 (2023) - 2022
- [c4]Joar Skalse, Lewis Hammond, Charlie Griffin, Alessandro Abate:
Lexicographic Multi-Objective Reinforcement Learning. IJCAI 2022: 3430-3436 - [c3]Joar Skalse, Nikolaus H. R. Howe, Dmitrii Krasheninnikov, David Krueger:
Defining and Characterizing Reward Gaming. NeurIPS 2022 - [i8]Joar Skalse, Matthew Farrugia-Roberts, Stuart Russell, Alessandro Abate, Adam Gleave:
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning. CoRR abs/2203.07475 (2022) - [i7]Joar Skalse, Nikolaus H. R. Howe, Dmitrii Krasheninnikov, David Krueger:
Defining and Characterizing Reward Hacking. CoRR abs/2209.13085 (2022) - [i6]Joar Skalse, Alessandro Abate:
Misspecification in Inverse Reinforcement Learning. CoRR abs/2212.03201 (2022) - [i5]Joar Skalse, Lewis Hammond, Charlie Griffin, Alessandro Abate:
Lexicographic Multi-Objective Reinforcement Learning. CoRR abs/2212.13769 (2022) - 2021
- [j1]Chris Mingard, Guillermo Valle Pérez, Joar Skalse, Ard A. Louis:
Is SGD a Bayesian sampler? Well, almost. J. Mach. Learn. Res. 22: 79:1-79:64 (2021) - [c2]Gavin Leech, Nandi Schoots, Joar Skalse:
Safety Properties of Inductive Logic Programming. SafeAI@AAAI 2021 - [c1]James Bell, Linda Linsefors, Caspar Oesterheld, Joar Skalse:
Reinforcement Learning in Newcomblike Environments. NeurIPS 2021: 22146-22157 - [i4]Joar Skalse:
A General Counterexample to Any Decision Theory and Some Responses. CoRR abs/2101.00280 (2021) - 2020
- [i3]Chris Mingard, Guillermo Valle Pérez, Joar Skalse, Ard A. Louis:
Is SGD a Bayesian sampler? Well, almost. CoRR abs/2006.15191 (2020)
2010 – 2019
- 2019
- [i2]Evan Hubinger, Chris van Merwijk, Vladimir Mikulik, Joar Skalse, Scott Garrabrant:
Risks from Learned Optimization in Advanced Machine Learning Systems. CoRR abs/1906.01820 (2019) - [i1]Chris Mingard, Joar Skalse, Guillermo Valle Pérez, David Martínez-Rubio, Vladimir Mikulik, Ard A. Louis:
Neural networks are a priori biased towards Boolean functions with low entropy. CoRR abs/1909.11522 (2019)
Coauthor Index
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