Phillip J.K. Christoffersen

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	All	Since 2019
Citations	449	449
h-index	6	6
i10-index	3	3

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20212022202320243 91 351

Co-authors

Dylan Hadfield-MenellMassachusetts Institute of TechnologyVerified email at csail.mit.edu
Andreas HauptStanford UniversityVerified email at stanford.edu
Rodrigo Toro IcartePontificia Universidad Católica de ChileVerified email at ing.puc.cl
Sheila McIlraithProfessor of Computer Science, University of TorontoVerified email at cs.toronto.edu
Rick ValenzanoAssistant Professor of Computer Science, Toronto Metropolitan UniversityVerified email at ryerson.ca
Toryn Q. KlassenUniversity of Toronto, Vector InstituteVerified email at cs.toronto.edu
Amir-massoud FarahmandPolytechnique Montreal, Mila, University of TorontoVerified email at cs.toronto.edu
Andrew LiUniversity of TorontoVerified email at cs.toronto.edu
Julian YocumUndergraduate, Massachusetts Institute of TechnologyVerified email at mit.edu
Justin SvegliatoSenior Research Scientist, Microsoft, UC BerkeleyVerified email at microsoft.com

Phillip J.K. Christoffersen

Other namesPhillip Christoffersen, Phillip Johannes Kerr Christoffersen, Phillip J Christoffersen

Massachusetts Institute of Technology

Verified email at mit.edu

Machine Learning AI Alignment Algorithms


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Open problems and fundamental limitations of reinforcement learning from human feedback S Casper, X Davies, C Shi, TK Gilbert, J Scheurer, J Rando, R Freedman, ... arXiv preprint arXiv:2307.15217, 2023	377	2023
Get It in Writing: Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL PJK Christoffersen, AA Haupt, D Hadfield-Menell Proc. of the 22nd International Conference on Autonomous Agents and …, 2022	30	2022
Open problems and fundamental limitations of reinforcement learning from human feedback. CoRR, abs/2307.15217, 2023. doi: 10.48550 S Casper, X Davies, C Shi, TK Gilbert, J Scheurer, J Rando, R Freedman, ... arXiv preprint ARXIV.2307.15217, 0	12
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback (arXiv: 2307.15217). arXiv S Casper, X Davies, C Shi, TK Gilbert, J Scheurer, J Rando, R Freedman, ...	7	2023
The act of remembering: a study in partially observable reinforcement learning RT Icarte, R Valenzano, TQ Klassen, P Christoffersen, A Farahmand, ... Challenges of Real World RL Workshop (RWRL), at NeurIPS 2020, 2020	7	2020
& Hadfield-Menell, D.(2023). Open problems and fundamental limitations of reinforcement learning from human feedback S Casper, X Davies, C Shi, TK Gilbert, J Scheurer, J Rando, R Freedman, ... arXiv preprint arXiv:2307.15217, 0	6
Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior PJK Christoffersen, AC Li, R Toro Icarte, SA McIlraith 4th Knowledge Representation and Reasoning Meets Machine Learning Workshop …, 2020	5	2020
Mitigating generative agent social dilemmas JR Yocum Massachusetts Institute of Technology, 2024	4	2024
Formal contracts mitigate social dilemmas in multi-agent reinforcement learning A Haupt, P Christoffersen, M Damani, D Hadfield-Menell Autonomous Agents and Multi-Agent Systems 38 (2), 1-38, 2024	1	2024
Mitigating Social Dilemmas in Multi-Agent Reinforcement Learning with Formal Contracting PJK Christoffersen Massachusetts Institute of Technology, 2024		2024
Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL AA Haupt, PJK Christoffersen, M Damani, D Hadfield-Menell arXiv e-prints, arXiv: 2208.10469, 2022		2022

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