| Large language models are human-level prompt engineers Y Zhou, AI Muresanu, Z Han, K Paster, S Pitis, H Chan, J Ba arXiv preprint arXiv:2211.01910, 2022 | 840 | 2022 |
| Inner monologue: Embodied reasoning through planning with language models W Huang, F Xia, T Xiao, H Chan, J Liang, P Florence, A Zeng, J Tompson, ... arXiv preprint arXiv:2207.05608, 2022 | 816 | 2022 |
| Maximum entropy gain exploration for long horizon multi-goal reinforcement learning S Pitis*, H Chan*, S Zhao, B Stadie, J Ba International Conference on Machine Learning, 7750-7761, 2020 | 138 | 2020 |
| Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models T Xiao*, H Chan*, P Sermanet, A Wahid, A Brohan, K Hausman, S Levine, ... arXiv preprint arXiv:2211.11736, 2022 | 69 | 2022 |
| An empirical study of stochastic gradient descent with structured covariance noise Y Wen, K Luk, M Gazeau, G Zhang, H Chan, J Ba International Conference on Artificial Intelligence and Statistics, 3621-3631, 2020 | 57* | 2020 |
| Steve-1: A generative model for text-to-behavior in minecraft S Lifshitz, K Paster, H Chan, J Ba, S McIlraith Advances in Neural Information Processing Systems 36, 2024 | 49 | 2024 |
| ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning H Chan, Y Wu, J Kiros, S Fidler, J Ba arXiv preprint arXiv:1902.04546, 2019 | 44 | 2019 |
| Large language models are human-level prompt engineers (2022) Y Zhou, AI Muresanu, Z Han, K Paster, S Pitis, H Chan, J Ba arXiv preprint arXiv:2211.01910, 2022 | 24 | 2022 |
| An inductive bias for distances: Neural nets that respect the triangle inequality S Pitis*, H Chan*, K Jamali, J Ba arXiv preprint arXiv:2002.05825, 2020 | 24 | 2020 |
| Large language models are human-level prompt engineers. arXiv Y Zhou, AI Muresanu, Z Han, K Paster, S Pitis, H Chan, J Ba Preprint posted online on November 3, 2022 | 20 | 2022 |
| Vision-language models as a source of rewards K Baumli, S Baveja, F Behbahani, H Chan, G Comanici, S Flennerhag, ... arXiv preprint arXiv:2312.09187, 2023 | 18 | 2023 |
| Learning domain invariant representations in goal-conditioned block mdps B Han, C Zheng, H Chan, K Paster, M Zhang, J Ba Advances in Neural Information Processing Systems 34, 764-776, 2021 | 17 | 2021 |
| Auto-regressive Graph Generation Modeling with Improved Evaluation Methods CC Liu, H Chan, K Luk, AI Borealis Graph Representation Learning Workshop at Neural Information Processing …, 2019 | 13 | 2019 |
| Steering large language models using APE Y Zhou, AI Muresanu, Z Han, K Paster, S Pitis, H Chan, J Ba NeurIPS ML Safety Workshop, 2022 | 5 | 2022 |
| Investigating the impact of intrusion detection system performance on communication latency and power system stability H Chan, E Hammad, D Kundur Proceedings of the Workshop on Communications, Computation and Control for …, 2016 | 5 | 2016 |
| Steve-1: A generative model for text-to-behavior in minecraft (abridged version) S Lifshitz, K Paster, H Chan, J Ba, S McIlraith NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning, 2023 | 4 | 2023 |
| Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels H Chan, J Kiros, W Chan arXiv preprint arXiv:2010.04438, 2020 | 4* | 2020 |
| ProtoGE: Prototype Goal Encodings for Multi-goal Reinforcement Learning S Pitis, H Chan, J Ba 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making, 2019 | 3 | 2019 |
| LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations A Ruoss, F Pardo, H Chan, B Li, V Mnih, T Genewein arXiv preprint arXiv:2412.01441, 2024 | | 2024 |
| Temporary Goals for Exploration H Xu, J Ba, S Pitis, H Chan Deep Reinforcement Learning Workshop NeurIPS 2022, 0 | | |
Jimmy BaUniversity of TorontoBestätigte E-Mail-Adresse bei cs.toronto.edu
