| A Note on the Inception Score S Barratt, R Sharma ICML 2018 Workshop on Theoretical Foundations and Applications of Deep …, 2018 | 837 | 2018 |
| Differentiable Convex Optimization Layers A Agrawal, B Amos, S Barratt, S Boyd, S Diamond, JZ Kolter Advances in Neural Information Processing Systems, 9558-9570, 2019 | 770 | 2019 |
| Differentiating Through a Cone Program A Agrawal, S Barratt, S Boyd, E Busseti, W Moursi Journal of Applied and Numerical Optimization 1 (2), 107-115, 2019 | 154 | 2019 |
| Learning probabilistic trajectory models of aircraft in terminal airspace from position data ST Barratt, MJ Kochenderfer, SP Boyd IEEE Transactions on Intelligent Transportation Systems 20 (9), 3536-3545, 2018 | 108 | 2018 |
| Learning Convex Optimization Control Policies A Agrawal, S Barratt, S Boyd, B Stellato Learning for Dynamics and Control, 361-373, 2020 | 76 | 2020 |
| On the differentiability of the solution to convex optimization problems S Barratt arXiv preprint arXiv:1804.05098, 2018 | 65 | 2018 |
| Learning convex optimization models A Agrawal, S Barratt, S Boyd IEEE/CAA Journal of Automatica Sinica 8 (8), 1355-1364, 2021 | 57 | 2021 |
| Fitting a Kalman Smoother to Data S Barratt, S Boyd American Control Conference, 1526-1531, 2020 | 48 | 2020 |
| Cooperative multi-agent reinforcement learning for low-level wireless communication C de Vrieze, S Barratt, D Tsai, A Sahai arXiv preprint arXiv:1801.04541, 2018 | 41 | 2018 |
| Least Squares Auto-Tuning S Barratt, S Boyd Engineering Optimization, 1-22, 2020 | 36 | 2020 |
| InterpNET: Neural Introspection for Interpretable Deep Learning S Barratt NIPS 2017 Interpretable Machine Learning Symposium, 2017 | 36 | 2017 |
| A Distributed Method for Fitting Laplacian Regularized Stratified Models J Tuck, S Barratt, S Boyd Journal of Machine Learning Research, to appear, 2019 | 30 | 2019 |
| Improved training with curriculum gans R Sharma, S Barratt, S Ermon, V Pande arXiv preprint arXiv:1807.09295, 2018 | 29 | 2018 |
| A note on the inception score. arXiv S Barratt, R Sharma arXiv preprint arXiv:1801.01973, 2018 | 24 | 2018 |
| Fitting a Linear Control Policy to Demonstrations with a Kalman Constraint M Palan, S Barratt, A McCauley, D Sadigh, V Sindhwani, S Boyd Learning for Decision and Control, 2020 | 23 | 2020 |
| Covariance prediction via convex optimization S Barratt, S Boyd Optimization and Engineering 24 (3), 2045-2078, 2023 | 19 | 2023 |
| Stochastic Control with Affine Dynamics and Extended Quadratic Costs S Barratt, S Boyd IEEE Transactions on Automatic Control, to appear, 2020 | 15 | 2020 |
| A non-rigid point and normal registration algorithm with applications to learning from demonstrations AX Lee, MA Goldstein, ST Barratt, P Abbeel 2015 IEEE International Conference on Robotics and Automation (ICRA), 935-942, 2015 | 15 | 2015 |
| Minimizing a Sum of Clipped Convex Functions S Barratt, G Angeris, S Boyd Optimization Letters 14 (8), 2443-2459, 2020 | 12 | 2020 |
| Optimal representative sample weighting S Barratt, G Angeris, S Boyd Statistics and Computing 31, 1-14, 2021 | 11 | 2021 |
Stephen BoydProfessor of Electrical Engineering, Computer Science, and Management Science, Stanford在 stanford.edu 的電子郵件地址已通過驗證
