| Backpropagation through the void: Optimizing control variates for black-box gradient estimation W Grathwohl, D Choi, Y Wu, G Roeder, D Duvenaud arXiv preprint arXiv:1711.00123, 2017 | 224 | 2017 |
| On empirical comparisons of optimizers for deep learning D Choi, CJ Shallue, Z Nado, J Lee, CJ Maddison, GE Dahl arXiv preprint arXiv:1910.05446, 2019 | 139 | 2019 |
| Guided evolutionary strategies: Augmenting random search with surrogate gradients N Maheswaranathan, L Metz, G Tucker, D Choi, J Sohl-Dickstein International Conference on Machine Learning, 4264-4273, 2019 | 50 | 2019 |
| Gradient estimation with stochastic softmax tricks M Paulus, D Choi, D Tarlow, A Krause, CJ Maddison Advances in Neural Information Processing Systems 33, 5691-5704, 2020 | 24 | 2020 |
| Faster neural network training with data echoing D Choi, A Passos, CJ Shallue, GE Dahl arXiv preprint arXiv:1907.05550, 2019 | 21 | 2019 |
| Guided evolutionary strategies: escaping the curse of dimensionality in random search N Maheswaranathan, L Metz, G Tucker, D Choi, J Sohl-Dickstein | 18 | 2018 |
| Self-tuning stochastic optimization with curvature-aware gradient filtering RTQ Chen, D Choi, L Balles, D Duvenaud, P Hennig PMLR, 2020 | 4 | 2020 |
| Systems and Methods for Reducing Idleness in a Machine-Learning Training System Using Data Echoing D Choi, AT Passos, CJ Shallue, GE Dahl US Patent App. 16/871,527, 2020 | | 2020 |
