| Gpt-4 technical report OpenAI arXiv preprint arXiv:2303.08774, 2023 | 7256* | 2023 |
| InfoVAE: Balancing Learning and Inference in Variational Autoencoders S Zhao, J Song, S Ermon Proceedings of the AAAI Conference on Artificial Intelligence, 2019 | 832* | 2019 |
| ChatGPT: Optimizing language models for dialogue J Schulman, B Zoph, C Kim, J Hilton, J Menick, J Weng, JFC Uribe, ... OpenAI blog 2 (4), 2022 | 356 | 2022 |
| Permutation Invariant Graph Generation via Score-Based Generative Modeling C Niu, Y Song, J Song, S Zhao, A Grover, S Ermon International Conference on Artificial Intelligence and Statistics, 2020 | 236 | 2020 |
| Learning controllable fair representations J Song, P Kalluri, A Grover, S Zhao, S Ermon The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 210 | 2019 |
| Towards deeper understanding of variational autoencoding models S Zhao, J Song, S Ermon arXiv preprint arXiv:1702.08658, 2017 | 205 | 2017 |
| Learning hierarchical features from generative models S Zhao, J Song, S Ermon Proceedings of the 34th International Conference on Machine Learning, PMLR …, 2017 | 173* | 2017 |
| A theory of usable information under computational constraints Y Xu, S Zhao, J Song, R Stewart, S Ermon International Conference on Learning Representations, 2019 | 160 | 2019 |
| Bias and generalization in deep generative models: An empirical study S Zhao, H Ren, A Yuan, J Song, N Goodman, S Ermon Proceedings of the 32nd International Conference on Neural Information …, 2018 | 157 | 2018 |
| Learning neural PDE solvers with convergence guarantees JT Hsieh, S Zhao, S Eismann, L Mirabella, S Ermon International Conference on Learning Representations, 2018 | 145 | 2018 |
| A-nice-mc: Adversarial training for mcmc J Song, S Zhao, S Ermon Proceedings of the 31st International Conference on Neural Information …, 2017 | 136 | 2017 |
| Amortized inference regularization R Shu, HH Bui, S Zhao, MJ Kochenderfer, S Ermon Proceedings of the 32nd International Conference on Neural Information …, 2018 | 106 | 2018 |
| The information autoencoding family: A lagrangian perspective on latent variable generative models S Zhao, J Song, S Ermon Proc. 34th Conference on Uncertainty in Artificial Intelligence, 2018 | 79 | 2018 |
| Domain adaptive imitation learning K Kim, Y Gu, J Song, S Zhao, S Ermon International Conference on Machine Learning, 5286-5295, 2020 | 77 | 2020 |
| Individual Calibration with Randomized Forecasting S Zhao, T Ma, S Ermon International Conference on Machine Learning, 2020 | 74 | 2020 |
| Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration S Zhao, MP Kim, R Sahoo, T Ma, S Ermon Proceedings of the 35th International Conference on Neural Information …, 2021 | 64 | 2021 |
| Sample-efficient safety assurances using conformal prediction R Luo, S Zhao, J Kuck, B Ivanovic, S Savarese, E Schmerling, M Pavone The International Journal of Robotics Research 43 (9), 1409-1424, 2024 | 59 | 2024 |
| Adaptive concentration inequalities for sequential decision problems S Zhao, E Zhou, A Sabharwal, S Ermon Advances in Neural Information Processing Systems 29, 2016 | 58 | 2016 |
| Introducing chatgpt J Schulman, B Zoph, C Kim, J Hilton, J Menick, J Weng, JFC Uribe, ... OpenAI Blog, 2022 | 45 | 2022 |
| Low-Degree Multicalibration P Gopalan, MP Kim, M Singhal, S Zhao 2022 Conference on Learning Theory, 2022 | 39 | 2022 |
Stefano ErmonStanford UniversityVerified email at cs.stanford.edu
