[18] M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017.
[19] J. Ho, A. Jain, and P. Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020.
[20] B. Ibarz, J. Leike, T. Pohlen, G. Irving, S. Legg, and D. Amodei. Reward learning from human preferences and demonstrations in atari, Nov 2018.
[21] Y. Kirstain, A. Polyak, U. Singer, S. Matiana, J. Penna, and O. Levy. Pick-a-pic: An open dataset of user preferences for text-to-image generation. 2023.
[22] J. Kreutzer, S. Khadivi, E. Matusov, and S. Riezler. Can neural machine translation be improved with user feedback? In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pages 92–105, 2018.
[23] K. Lee, H. Liu, M. Ryu, O. Watkins, Y. Du, C. Boutilier, P. Abbeel, M. Ghavamzadeh, and S. S. Gu. Aligning text-to-image models using human feedback. arXiv preprint arXiv:2302.12192, 2023.
[24] K. Lee, L. M. Smith, and P. Abbeel. Pebble: Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training. In International Conference on Machine Learning, pages 6152-6163. PMLR, 2021.
[25] B. Lester, R. Al-Rfou, and N. Constant. The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691, 2021.
[26] J. Li, D. Li, C. Xiong, and S. Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning, pages 12888-12900. PMLR, 2022.
[27] X. L. Li and P. Liang. Prefix-tuning: Optimizing continuous prompts for generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4582-4597, 2021.
[28] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014.
[29] H. Liu, C. Sferrazza, and P. Abbeel. Chain of hindsight aligns language models with feedback, Feb 2023.
[30] L. Liu, Y. Ren, Z. Lin, and Z. Zhao. Pseudo numerical methods for diffusion models on manifolds. In International Conference on Learning Representations, 2022.
[31] N. Liu, S. Li, Y. Du, A. Torralba, and J. Tenenbaum. Compositional visual generation with composable diffusion models, Jun 2022.
[32] X. Liu, K. Ji, Y. Fu, Z. Du, Z. Yang, and J. Tang. P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks. arXiv preprint arXiv:2110.07602, 2021.
[33] X. Liu, F. Zhang, Z. Hou, L. Mian, Z. Wang, J. Zhang, and J. Tang. Self-supervised learning: Generative or contrastive. IEEE Transactions on Knowledge and Data Engineering, 35(1):857-876, 2021.
[34] X. Liu, Y. Zheng, Z. Du, M. Ding, Y. Qian, Z. Yang, and J. Tang. Gpt understands, too. arXiv preprint arXiv:2103.10385, 2021.
[35] J. MacGlashan, M. Ho, R. Loftin, B. Peng, G. Wang, D. Roberts, M. Taylor, and M. Littman. Interactive learning from policy-dependent human feedback, Aug 2017.