Cong Lu

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Philip J. BallGoogle DeepMindVerified email at google.com
Jack Parker-HolderGoogle DeepMind, UCLVerified email at google.com
Shimon WhitesonProfessor of Computer Science, University of Oxford / Senior Staff Research Scientist, WaymoVerified email at cs.ox.ac.uk
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
Robert Tjarko LangeSakana AI, TU BerlinVerified email at tu-berlin.de
Jeff CluneProfessor, Comp. Sci.,U British Columbia; CIFAR AI Chair, Vector; Senior Research Advisor, DeepMindVerified email at ubc.ca
Chris LuOpenAIVerified email at openai.com
Maximilian IglWaymo ResearchVerified email at eng.ox.ac.uk
Tim G. J. RudnerFaculty Fellow, New York UniversityVerified email at nyu.edu
Luisa ZintgrafDeepMindVerified email at deepmind.com
Xingchen WanGoogleVerified email at google.com
Jakob FoersterAssociate Professor, University of OxfordVerified email at eng.ox.ac.uk
David HaSakana AIVerified email at sakana.ai
Shengran HuUniversity of British ColumbiaVerified email at cs.ubc.ca
Raluca GeorgescuMicrosoft ResearchVerified email at microsoft.com
Matthew T. JacksonUniversity of OxfordVerified email at robots.ox.ac.uk
Risto VuorioUniversity of OxfordVerified email at cs.ox.ac.uk
Karmesh YadavPh.D. Student at Georgia TechVerified email at gatech.edu
Mattie FellowsPostdoctoral Researcher, Foerster Lab for AI Research (FLAIR), University of OxfordVerified email at eng.ox.ac.uk

Cong Lu

University of British Columbia

Verified email at cs.ubc.ca - Homepage

Reinforcement Learning Open-Endedness Generative Modeling Deep Learning


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The AI Scientist: Towards fully automated open-ended scientific discovery C Lu, C Lu, RT Lange, J Foerster, J Clune, D Ha arXiv preprint arXiv:2408.06292, 2024	68*	2024
Revisiting Design Choices in Offline Model Based Reinforcement Learning C Lu, PJ Ball, J Parker-Holder, M Osborne, S Roberts ICLR 2022 (Spotlight), 2022	65*	2022
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces X Wan, V Nguyen, H Ha, B Ru, C Lu, MA Osborne International Conference on Machine Learning (ICML) 38, 10663-10674, 2021	61	2021
Exploration in approximate hyper-state space for meta reinforcement learning LM Zintgraf, L Feng, C Lu, M Igl, K Hartikainen, K Hofmann, S Whiteson International Conference on Machine Learning, 12991-13001, 2020	55	2020
Synthetic Experience Replay C Lu, PJ Ball, YW Teh, J Parker-Holder NeurIPS 2023, 2023	50	2023
Varibad: Variational bayes-adaptive deep rl via meta-learning L Zintgraf, S Schulze, C Lu, L Feng, M Igl, K Shiarlis, Y Gal, K Hofmann, ... Journal of Machine Learning Research 22 (289), 1-39, 2021	50	2021
Challenges and opportunities in offline reinforcement learning from visual observations C Lu, PJ Ball, TGJ Rudner, J Parker-Holder, MA Osborne, YW Teh Transactions on Machine Learning Research, 2022	44	2022
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment PJ Ball, C Lu, J Parker-Holder, S Roberts International Conference on Machine Learning (ICML) 38, 2021	40	2021
On pathologies in KL-regularized reinforcement learning from expert demonstrations TGJ Rudner, C Lu, M Osborne, Y Gal, Y Teh Advances in Neural Information Processing Systems 34, 2021	27	2021
Bayesian Generational Population-Based Training X Wan, C Lu, J Parker-Holder, PJ Ball, V Nguyen, B Ru, MA Osborne AutoML 2022, 2022	22	2022
Automated design of agentic systems S Hu, C Lu, J Clune arXiv preprint arXiv:2408.08435, 2024	13	2024
Go-Explore Complex 3-D Game Environments for Automated Reachability Testing C Lu, R Georgescu, J Verwey IEEE Transactions on Games 16 (1), 235-240, 2022	9	2022
Policy-Guided Diffusion MT Jackson, MT Matthews, C Lu, B Ellis, S Whiteson, J Foerster Reinforcement Learning Conference 2024 (Oral), 2024	6	2024
Video Diffusion Models: A Survey A Melnik, M Ljubljanac, C Lu, Q Yan, W Ren, H Ritter Transactions on Machine Learning Research, 2024	5	2024
Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Models C Lu, S Hu, J Clune AutoRL Workshop, ICML 2024, 2024	2	2024
Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control G Gupta, K Yadav, Y Gal, D Batra, Z Kira, C Lu, TGJ Rudner NeurIPS 2024 (Spotlight), 2024	1	2024
IGDrivSim: A Benchmark for the Imitation Gap in Autonomous Driving C Grislain, R Vuorio, C Lu, S Whiteson arXiv preprint arXiv:2411.04653, 2024		2024
A Bayesian Solution To The Imitation Gap R Vuorio, M Fellows, C Lu, C Grislain, S Whiteson arXiv preprint arXiv:2407.00495, 2024		2024
The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning A Sims, C Lu, YW Teh NeurIPS 2024, 2024		2024
Quality-Diversity Self-Play: Open-Ended Strategy Innovation via Foundation Models A Dharna, C Lu, J Clune NeurIPS 2024 Workshop on Open-World Agents, 2024		2024

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