| A DIRT-T Approach to Unsupervised Domain Adaptation R Shu, HH Bui, H Narui, S Ermon International Conference on Learning Representations (ICLR), 2018 | 713 | 2018 |
| SHANK3 and IGF1 restore synaptic deficits in neurons from 22q13 deletion syndrome patients A Shcheglovitov, O Shcheglovitova, M Yazawa, T Portmann, R Shu, ... Nature 503 (7475), 267-271, 2013 | 508 | 2013 |
| Constructing unrestricted adversarial examples with generative models Y Song, R Shu, N Kushman, S Ermon Advances in neural information processing systems 31, 2018 | 327 | 2018 |
| Weakly supervised disentanglement with guarantees R Shu, Y Chen, A Kumar, S Ermon, B Poole arXiv preprint arXiv:1910.09772, 2019 | 161 | 2019 |
| Fair generative modeling via weak supervision K Choi, A Grover, T Singh, R Shu, S Ermon International Conference on Machine Learning, 1887-1898, 2020 | 146* | 2020 |
| Robust locally-linear controllable embedding E Banijamali, R Shu, M Ghavamzadeh, H Bui, A Ghodsi International Conference on Artificial Intelligence and Statistics (AISTATS), 2017 | 111 | 2017 |
| Amortized Inference Regularization R Shu, HH Bui, S Zhao, MJ Kochenderfer, S Ermon Neural Information Processing Systems (NIPS), 2018 | 106 | 2018 |
| INF2-mediated severing through actin filament encirclement and disruption PS Gurel, P Ge, EE Grintsevich, R Shu, L Blanchoin, ZH Zhou, E Reisler, ... Current Biology 24 (2), 156-164, 2014 | 68 | 2014 |
| Alignflow: Cycle consistent learning from multiple domains via normalizing flows A Grover, C Chute, R Shu, Z Cao, S Ermon Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4028-4035, 2020 | 61 | 2020 |
| Temporal predictive coding for model-based planning in latent space TD Nguyen, R Shu, T Pham, H Bui, S Ermon International Conference on Machine Learning, 8130-8139, 2021 | 57 | 2021 |
| Assembly and turnover of short actin filaments by the formin INF2 and profilin PS Gurel, A Mu, B Guo, R Shu, DF Mierke, HN Higgs Journal of Biological Chemistry 290 (37), 22494-22506, 2015 | 39 | 2015 |
| Prediction, consistency, curvature: Representation learning for locally-linear control N Levine, Y Chow, R Shu, A Li, M Ghavamzadeh, H Bui arXiv preprint arXiv:1909.01506, 2019 | 34 | 2019 |
| Bottleneck conditional density estimation R Shu, HH Bui, M Ghavamzadeh International Conference on Machine Learning (ICML), 2016 | 31 | 2016 |
| AC-GAN Learns a Biased Distribution R Shu, H Bui, S Ermon NIPS Workshop on Bayesian Deep Learning, 2017 | 30 | 2017 |
| Stochastic video prediction with conditional density estimation R Shu, J Brofos, F Zhang, HH Bui, M Ghavamzadeh, M Kochenderfer ECCV Workshop on Action and Anticipation for Visual Learning 2, 2, 2016 | 30 | 2016 |
| Monitoring ATP hydrolysis and ATPase inhibitor screening using 1H NMR B Guo, PS Gurel, R Shu, HN Higgs, M Pellegrini, DF Mierke Chemical Communications 50 (81), 12037-12039, 2014 | 30 | 2014 |
| Bayesian optimization and attribute adjustment S Eissman, D Levy, R Shu, S Bartzsch, S Ermon Proc. 34th Conference on Uncertainty in Artificial Intelligence, 2018 | 29 | 2018 |
| Predictive coding for locally-linear control R Shu, T Nguyen, Y Chow, T Pham, K Than, M Ghavamzadeh, S Ermon, ... International Conference on Machine Learning, 8862-8871, 2020 | 27 | 2020 |
| Generative Adversarial Examples Y Song, R Shu, N Kushman, S Ermon Neural Information Processing Systems (NIPS), 2018 | 22 | 2018 |
| Anytime sampling for autoregressive models via ordered autoencoding Y Xu, Y Song, S Garg, L Gong, R Shu, A Grover, S Ermon arXiv preprint arXiv:2102.11495, 2021 | 19 | 2021 |