| The ethics of advanced ai assistants I Gabriel, A Manzini, G Keeling, LA Hendricks, V Rieser, H Iqbal, ... arXiv preprint arXiv:2404.16244, 2024 | 6 | 2024 |
| Generating training datasets for training neural networks FP Such, A Rawal, JA Lehman, KO Stanley, JM Clune US Patent 11,907,675, 2024 | | 2024 |
| The openelm library: Leveraging progress in language models for novel evolutionary algorithms H Bradley, H Fan, T Galanos, R Zhou, D Scott, J Lehman Genetic Programming Theory and Practice XX, 177-201, 2024 | 5 | 2024 |
| Deep reinforcement learning based models for hard-exploration problems JM Clune, AL Ecoffet, KO Stanley, J Huizinga, JA Lehman US Patent 11,829,870, 2023 | 1 | 2023 |
| Evolution through large models J Lehman, J Gordon, S Jain, K Ndousse, C Yeh, KO Stanley Handbook of Evolutionary Machine Learning, 331-366, 2023 | 58 | 2023 |
| Quality-diversity through ai feedback H Bradley, A Dai, H Teufel, J Zhang, K Oostermeijer, M Bellagente, ... arXiv preprint arXiv:2310.13032, 2023 | 19 | 2023 |
| Quality diversity through human feedback L Ding, J Zhang, J Clune, L Spector, J Lehman arXiv preprint arXiv:2310.12103, 2023 | 11 | 2023 |
| Omni: Open-endedness via models of human notions of interestingness J Zhang, J Lehman, K Stanley, J Clune arXiv preprint arXiv:2306.01711, 2023 | 16 | 2023 |
| Language model crossover: Variation through few-shot prompting E Meyerson, MJ Nelson, H Bradley, A Gaier, A Moradi, AK Hoover, ... arXiv preprint arXiv:2302.12170, 2023 | 35 | 2023 |
| Machine love J Lehman arXiv preprint arXiv:2302.09248, 2023 | 4 | 2023 |
| Training neural networks using evolution based strategies and novelty search E Conti, V Madhavan, JM Clune, FP Such, JA Lehman, KO Stanley US Patent 11,068,787, 2021 | 2 | 2021 |
| Reinforcement learning under moral uncertainty A Ecoffet, J Lehman International conference on machine learning, 2926-2936, 2021 | 34 | 2021 |
| First return, then explore A Ecoffet, J Huizinga, J Lehman, KO Stanley, J Clune Nature 590 (7847), 580-586, 2021 | 358 | 2021 |
| Enhanced poet: Open-ended reinforcement learning through unbounded invention of learning challenges and their solutions R Wang, J Lehman, A Rawal, J Zhi, Y Li, J Clune, K Stanley International conference on machine learning, 9940-9951, 2020 | 109 | 2020 |
| Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data FP Such, A Rawal, J Lehman, K Stanley, J Clune International Conference on Machine Learning, 9206-9216, 2020 | 170 | 2020 |
| Learning belief representations for imitation learning in pomdps T Gangwani, J Lehman, Q Liu, J Peng uncertainty in artificial intelligence, 1061-1071, 2020 | 39 | 2020 |
| Ideas for Improving the Field of Machine Learning: Summarizing Discussion from the NeurIPS 2019 Retrospectives Workshop S Sodhani, MS Jaiswal, L Baker, K Sinha, C Shneider, P Henderson, ... arXiv preprint arXiv:2007.10546, 2020 | 1 | 2020 |
| Antimander: open source detection of gerrymandering though multi-objective evolutionary algorithms J Simon, J Lehman Proceedings of the 2020 Genetic and Evolutionary Computation Conference …, 2020 | 3 | 2020 |
| Open questions in creating safe open-ended AI: tensions between control and creativity A Ecoffet, J Clune, J Lehman Artificial Life Conference Proceedings 32, 27-35, 2020 | 16 | 2020 |
| Training of artificial neural networks using safe mutations based on output gradients JA Lehman, KO Stanley, JM Clune US Patent 10,699,195, 2020 | 18 | 2020 |
Kenneth O. StanleyResearch Manager, OpenAIVerified email at openai.com