David Scott Krueger 个人学术档案

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引用	7914	7366
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Tegan MaharajAssistant Professor at University of Toronto在 polymtl.ca 的电子邮件经过验证
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFAR在 umontreal.ca 的电子邮件经过验证
Aaron CourvilleProfessor, DIRO, Université de Montréal, Mila, Cifar CAI chair在 umontreal.ca 的电子邮件经过验证
Nicolas BallasMeta AI Research在 meta.com 的电子邮件经过验证
Stanisław JastrzębskiChief Technology Officer & Chief Scientist @ Molecule.One在 molecule.one 的电子邮件经过验证
Devansh ArpitRashi.ai在 rashi.ai 的电子邮件经过验证
Asja FischerProfessor for Machine Learning, Ruhr University Bochum在 ini.rub.de 的电子邮件经过验证
Max KanwalPhD Student, Stanford University在 stanford.edu 的电子邮件经过验证
Emmanuel BengioMcGill University在 mail.mcgill.ca 的电子邮件经过验证
Alexandre LacosteStaff Research Scientist, ServiceNow Research在 servicenow.com 的电子邮件经过验证
Jan LeikeOpenAI在 openai.com 的电子邮件经过验证
Dmitrii KrasheninnikovUniversity of Cambridge在 cam.ac.uk 的电子邮件经过验证
Laurent DinhApple在 apple.com 的电子邮件经过验证
Ethan CaballeroMila (Montreal Institute for Learning Algorithms)在 mila.quebec 的电子邮件经过验证
Simon Lacoste-JulienAssociate Professor - Canada CIFAR AI Chair, University of Montreal / Mila在 iro.umontreal.ca 的电子邮件经过验证
Chin-Wei HuangMicrosoft Research在 microsoft.com 的电子邮件经过验证
Lauro LangoscoUniversity of Cambridge在 cam.ac.uk 的电子邮件经过验证
Jörn-Henrik JacobsenApple AI/ML Research在 vectorinstitute.ai 的电子邮件经过验证
Amy ZhangAssistant Professor of Electrical and Computer Engineering at University of Texas at Austin在 austin.utexas.edu 的电子邮件经过验证
Jonathan BinasVivid Machines在 iro.umontreal.ca 的电子邮件经过验证

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David Scott Krueger

University Assistant Professor, University of Cambridge

在 cam.ac.uk 的电子邮件经过验证 - 首页

AI Alignment Deep Learning


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Nice: Non-linear independent components estimation L Dinh, D Krueger, Y Bengio arXiv preprint arXiv:1410.8516, 2014	2300	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	1917*	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	792	2021
Neural autoregressive flows CW Huang, D Krueger, A Lacoste, A Courville International Conference on Machine Learning (ICML) 2018, 2018	503	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	380	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	346	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	257	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	209	2023
Bayesian hypernetworks D Krueger, CW Huang, R Islam, R Turner, A Lacoste, A Courville arXiv preprint arXiv:1710.04759, 2017	165	2017
Defining and characterizing reward gaming J Skalse, N Howe, D Krasheninnikov, D Krueger Advances in Neural Information Processing Systems 35, 9460-9471, 2022	120	2022
Zero-bias autoencoders and the benefits of co-adapting features K Konda, R Memisevic, D Krueger International Conference on Learning Representations (ICLR) 2015, 2014	106*	2014
Nested lstms JRA Moniz, D Krueger Asian Conference on Machine Learning, 530-544, 2017	87	2017
Regularizing rnns by stabilizing activations D Krueger, R Memisevic International Conference on Learning Representations (ICLR) 2016, 2015	81	2015
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	75	2022
Hidden Incentives for Auto-Induced Distributional Shift D Krueger, T Maharaj, J Leike arXiv preprint arXiv:2009.09153, 2020	58*	2020
Broken neural scaling laws E Caballero, K Gupta, I Rish, D Krueger arXiv preprint arXiv:2210.14891, 2022	50	2022
AI research considerations for human existential safety (ARCHES) A Critch, D Krueger arXiv preprint arXiv:2006.04948, 2020	50	2020
Managing ai risks in an era of rapid progress Y Bengio, G Hinton, A Yao, D Song, P Abbeel, YN Harari, YQ Zhang, ... arXiv preprint arXiv:2310.17688, 2023	44	2023
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	36	2023
Mechanistic mode connectivity ES Lubana, EJ Bigelow, RP Dick, D Krueger, H Tanaka International Conference on Machine Learning, 22965-23004, 2023	35	2023

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