Lu, Cong

31 publications

ICLRW 2025 Automated Capability Discovery via Model Self-Exploration Cong Lu, Shengran Hu, Jeff Clune

ICLR 2025 Automated Design of Agentic Systems Shengran Hu, Cong Lu, Jeff Clune

ICLR 2025 Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Models Cong Lu, Shengran Hu, Jeff Clune

ICLRW 2025 StochasTok: Improving Fine-Grained Subword Understanding in LLMs Anya Sims, Cong Lu, Klara Kaleb, Jakob Nicolaus Foerster, Yee Whye Teh

NeurIPSW 2024 Automated Design of Agentic Systems Shengran Hu, Cong Lu, Jeff Clune

NeurIPSW 2024 Automated Design of Agentic Systems Shengran Hu, Cong Lu, Jeff Clune

NeurIPSW 2024 Automated Design of Agentic Systems Shengran Hu, Cong Lu, Jeff Clune

NeurIPSW 2024 Automated Design of Agentic Systems Shengran Hu, Cong Lu, Jeff Clune

NeurIPSW 2024 Beyond Benchmarking: Automated Capability Discovery via Model Self-Exploration Cong Lu, Shengran Hu, Jeff Clune

ICMLW 2024 Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Models Cong Lu, Shengran Hu, Jeff Clune

NeurIPS 2024 Pre-Trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control Gunshi Gupta, Karmesh Yadav, Yarin Gal, Zsolt Kira, Dhruv Batra, Cong Lu, Tim G. J. Rudner

ICLRW 2024 Pre-Trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control Gunshi Gupta, Karmesh Yadav, Yarin Gal, Dhruv Batra, Zsolt Kira, Cong Lu, Tim G. J. Rudner

NeurIPSW 2024 Quality-Diversity Self-Play: Open-Ended Strategy Innovation via Foundation Models Aaron Dharna, Cong Lu, Jeff Clune

NeurIPSW 2024 Quality-Diversity Self-Play: Open-Ended Strategy Innovation via Foundation Models Aaron Dharna, Cong Lu, Jeff Clune

NeurIPS 2024 The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning Anya Sims, Cong Lu, Jakob N. Foerster, Yee Whye Teh

TMLR 2024 Video Diffusion Models: A Survey Andrew Melnik, Michal Ljubljanac, Cong Lu, Qi Yan, Weiming Ren, Helge Ritter

TMLR 2023 Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A Osborne, Yee Whye Teh

NeurIPS 2023 Synthetic Experience Replay Cong Lu, Philip Ball, Yee Whye Teh, Jack Parker-Holder

ICLRW 2023 Synthetic Experience Replay Cong Lu, Philip J. Ball, Jack Parker-Holder

ICMLW 2023 Synthetic Experience Replay Cong Lu, Philip J. Ball, Yee Whye Teh, Jack Parker-Holder

NeurIPSW 2023 The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning Anya Sims, Cong Lu, Yee Whye Teh

AutoML 2022 Bayesian Generational Population-Based Training Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael Osborne

ICLRW 2022 Bayesian Generational Population-Based Training Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael Osborne

ICMLW 2022 Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A Osborne, Yee Whye Teh

ICLR 2022 Revisiting Design Choices in Offline Model Based Reinforcement Learning Cong Lu, Philip Ball, Jack Parker-Holder, Michael Osborne, Stephen J. Roberts

ICML 2021 Augmented World Models Facilitate Zero-Shot Dynamics Generalization from a Single Offline Environment Philip J Ball, Cong Lu, Jack Parker-Holder, Stephen Roberts

ICLRW 2021 Augmented World Models Facilitate Zero-Shot Dynamics Generalization from a Single Offline Environment Philip Ball, Cong Lu, Jack Parker-Holder, S Roberts

ICML 2021 Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning Luisa M Zintgraf, Leo Feng, Cong Lu, Maximilian Igl, Kristian Hartikainen, Katja Hofmann, Shimon Whiteson

NeurIPS 2021 On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations Tim G. J. Rudner, Cong Lu, Michael A Osborne, Yarin Gal, Yee W. Teh

ICML 2021 Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces Xingchen Wan, Vu Nguyen, Huong Ha, Binxin Ru, Cong Lu, Michael A. Osborne

JMLR 2021 VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning Luisa Zintgraf, Sebastian Schulze, Cong Lu, Leo Feng, Maximilian Igl, Kyriacos Shiarlis, Yarin Gal, Katja Hofmann, Shimon Whiteson