David Scott Krueger

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Citations	8857	8305
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i10-index	34	34

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Tegan MaharajAssistant Professor at MilaVerified email at polymtl.ca
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
Aaron CourvilleProfessor, DIRO, Université de Montréal, Mila, Cifar CAI chairVerified email at umontreal.ca
Nicolas BallasMeta AI ResearchVerified email at meta.com
Laurent DinhAppleVerified email at apple.com
Jan LeikeOpenAIVerified email at openai.com
Dmitrii KrasheninnikovUniversity of CambridgeVerified email at cam.ac.uk
Stanisław JastrzębskiChief Technology Officer & Chief Scientist @ Molecule.OneVerified email at molecule.one
Asja FischerProfessor for Machine Learning, Ruhr University BochumVerified email at ini.rub.de
Max KanwalPhD Student, Stanford UniversityVerified email at stanford.edu
Emmanuel BengioMcGill University; Recursion/Valence LabsVerified email at mail.mcgill.ca
Alexandre LacosteStaff Research Scientist, ServiceNow ResearchVerified email at servicenow.com
Ethan CaballeroMila (Montreal Institute for Learning Algorithms)Verified email at mila.quebec
Simon Lacoste-JulienAssociate Professor - Canada CIFAR AI Chair, University of Montreal / MilaVerified email at iro.umontreal.ca
Chin-Wei HuangMicrosoft ResearchVerified email at microsoft.com
Lauro LangoscoUniversity of CambridgeVerified email at cam.ac.uk
Shoaib Ahmed SiddiquiUniversity of CambridgeVerified email at cam.ac.uk
Jörn-Henrik JacobsenApple AI/ML ResearchVerified email at vectorinstitute.ai
Amy ZhangAssistant Professor of Electrical and Computer Engineering at University of Texas at AustinVerified email at austin.utexas.edu
Jonathan BinasVivid MachinesVerified email at iro.umontreal.ca

David Scott Krueger

University Assistant Professor, University of Cambridge

Verified email at cam.ac.uk - Homepage

AI Alignment Deep Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Nice: Non-linear independent components estimation L Dinh, D Krueger, Y Bengio arXiv preprint arXiv:1410.8516, 2014	2471	2014
A closer look at memorization in deep networks D Krueger, N Ballas, S Jastrzebski, D Arpit, MS Kanwal, T Maharaj, ... International Conference on Machine Learning (ICML) 2017, 2017	2053*	2017
Out-of-distribution generalization via risk extrapolation (rex) D Krueger, E Caballero, JH Jacobsen, A Zhang, J Binas, D Zhang, ... International conference on machine learning, 5815-5826, 2021	868	2021
Neural autoregressive flows CW Huang, D Krueger, A Lacoste, A Courville International Conference on Machine Learning (ICML) 2018, 2018	531	2018
Zoneout: Regularizing rnns by randomly preserving hidden activations D Krueger, T Maharaj, J Kramár, M Pezeshki, N Ballas, NR Ke, A Goyal, ... International Conference on Learning Representations (ICLR) 2017, 2016	386	2016
Toward trustworthy AI development: mechanisms for supporting verifiable claims M Brundage, S Avin, J Wang, H Belfield, G Krueger, G Hadfield, H Khlaaf, ... arXiv preprint arXiv:2004.07213, 2020	380	2020
Scalable agent alignment via reward modeling: a research direction J Leike, D Krueger, T Everitt, M Martic, V Maini, S Legg arXiv preprint arXiv:1811.07871, 2018	308	2018
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	297	2023
Bayesian hypernetworks D Krueger, CW Huang, R Islam, R Turner, A Lacoste, A Courville arXiv preprint arXiv:1710.04759, 2017	176	2017
Defining and characterizing reward gaming J Skalse, N Howe, D Krasheninnikov, D Krueger Advances in Neural Information Processing Systems 35, 9460-9471, 2022	170	2022
Managing extreme AI risks amid rapid progress Y Bengio, G Hinton, A Yao, D Song, P Abbeel, T Darrell, YN Harari, ... Science 384 (6698), 842-845, 2024	126*	2024
Zero-bias autoencoders and the benefits of co-adapting features K Konda, R Memisevic, D Krueger International Conference on Learning Representations (ICLR) 2015, 2014	107*	2014
Goal misgeneralization in deep reinforcement learning LL Di Langosco, J Koch, LD Sharkey, J Pfau, D Krueger International Conference on Machine Learning, 12004-12019, 2022	99	2022
Nested lstms JRA Moniz, D Krueger Asian Conference on Machine Learning, 530-544, 2017	90	2017
Regularizing rnns by stabilizing activations D Krueger, R Memisevic International Conference on Learning Representations (ICLR) 2016, 2015	83	2015
Hidden Incentives for Auto-Induced Distributional Shift D Krueger, T Maharaj, J Leike arXiv preprint arXiv:2009.09153, 2020	59*	2020
Broken neural scaling laws E Caballero, K Gupta, I Rish, D Krueger arXiv preprint arXiv:2210.14891, 2022	57	2022
AI research considerations for human existential safety (ARCHES) A Critch, D Krueger arXiv preprint arXiv:2006.04948, 2020	56	2020
Harms from increasingly agentic algorithmic systems A Chan, R Salganik, A Markelius, C Pang, N Rajkumar, D Krasheninnikov, ... Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023	51	2023
Reward model ensembles help mitigate overoptimization T Coste, U Anwar, R Kirk, D Krueger arXiv preprint arXiv:2310.02743, 2023	50	2023

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