| Infovae: Balancing learning and inference in variational autoencoders S Zhao, J Song, S Ermon Proceedings of the aaai conference on artificial intelligence 33 (01), 5885-5892, 2019 | 515* | 2019 |
| Infogail: Interpretable imitation learning from visual demonstrations Y Li, J Song, S Ermon Advances in Neural Information Processing Systems 30, 2017 | 350* | 2017 |
| Towards deeper understanding of variational autoencoding models S Zhao, J Song, S Ermon arXiv preprint arXiv:1702.08658, 2017 | 134 | 2017 |
| Denoising diffusion implicit models J Song, C Meng, S Ermon International Conference on Learning Representations (ICLR) 2021, 2020 | 128 | 2020 |
| Multi-agent generative adversarial imitation learning J Song, H Ren, D Sadigh, S Ermon Neural Information Processing Systems (NeurIPS) 2018, 2018 | 127 | 2018 |
| Learning hierarchical features from deep generative models S Zhao, J Song, S Ermon International Conference on Machine Learning, 4091-4099, 2017 | 120* | 2017 |
| Learning controllable fair representations J Song, P Kalluri, A Grover, S Zhao, S Ermon The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 112 | 2019 |
| Max-margin nonparametric latent feature models for link prediction J Zhu, J Song, B Chen arXiv preprint arXiv:1602.07428, 2016 | 107 | 2016 |
| Understanding the limitations of variational mutual information estimators J Song, S Ermon International Conference on Learning Representations (ICLR) 2020, 2019 | 105 | 2019 |
| A-nice-mc: Adversarial training for mcmc J Song, S Zhao, S Ermon Neural Information Processing Systems (NeurIPS) 2017, 2017 | 91 | 2017 |
| Bias correction of learned generative models using likelihood-free importance weighting A Grover, J Song, A Agarwal, K Tran, A Kapoor, E Horvitz, S Ermon Neural Information Processing Systems (NeurIPS) 2019, 2019 | 84 | 2019 |
| Bias and generalization in deep generative models: An empirical study S Zhao, H Ren, A Yuan, J Song, N Goodman, S Ermon Neural Information Processing Systems (NeurIPS) 2018, 2018 | 72 | 2018 |
| Multi-agent adversarial inverse reinforcement learning L Yu, J Song, S Ermon International Conference on Machine Learning, 7194-7201, 2019 | 67 | 2019 |
| The information autoencoding family: A lagrangian perspective on latent variable generative models S Zhao, J Song, S Ermon Conference on Uncertainty in Artificial Intelligence (UAI) 2018, 2018 | 55 | 2018 |
| A theory of usable information under computational constraints Y Xu, S Zhao, J Song, R Stewart, S Ermon nternational Conference on Learning Representations (ICLR) 2020, 2020 | 43 | 2020 |
| Calibrated model-based deep reinforcement learning A Malik, V Kuleshov, J Song, D Nemer, H Seymour, S Ermon International Conference on Machine Learning, 4314-4323, 2019 | 39 | 2019 |
| Negative data augmentation A Sinha, K Ayush, J Song, B Uzkent, H Jin, S Ermon International Conference on Learning Representations (ICLR) 2021, 2021 | 34 | 2021 |
| 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, 4474-4484, 2020 | 34 | 2020 |
| Bridging the gap between f-gans and wasserstein gans J Song, S Ermon International Conference on Machine Learning, 9078-9087, 2020 | 30 | 2020 |
| Sdedit: Image synthesis and editing with stochastic differential equations C Meng, Y Song, J Song, J Wu, JY Zhu, S Ermon arXiv preprint arXiv:2108.01073, 2021 | 28 | 2021 |
Stefano ErmonStanford UniversityVerified email at cs.stanford.edu
