| Reachable polyhedral marching (rpm): A safety verification algorithm for robotic systems with deep neural network components JA Vincent, M Schwager 2021 IEEE International Conference on Robotics and Automation (ICRA), 9029-9035, 2021 | 35 | 2021 |
| Dinno: Distributed neural network optimization for multi-robot collaborative learning J Yu, JA Vincent, M Schwager IEEE Robotics and Automation Letters 7 (2), 1896-1903, 2022 | 31 | 2022 |
| Mac Schwager. Reachable polyhedral marching (rpm): A safety verification algorithm for robotic systems with deep neural network components JA Vincent 2021 IEEE International Conference on Robotics and Automation (ICRA), 9029-9035, 2021 | 12 | 2021 |
| Beamforming sensitivity of airborne distributed arrays to flight tracking and vehicle dynamics JA Vincent, EJ Arnold 2017 IEEE Aerospace Conference, 1-14, 2017 | 7 | 2017 |
| Reachable polyhedral marching (rpm): An exact analysis tool for deep-learned control systems JA Vincent, M Schwager arXiv preprint arXiv:2210.08339, 2022 | 4 | 2022 |
| Guarantees on Robot System Performance Using Stochastic Simulation Rollouts JA Vincent, AO Feldman, M Schwager arXiv preprint arXiv:2309.10874, 2023 | 1 | 2023 |
| Using Different Machine Learning Algorithms to Predict the Prices of Flight Tickets JR Bollack, JA Vincent Journal of Student Research 12 (4), 2023 | | 2023 |
| Full-Distribution Generalization Bounds for Imitation Learning Policies JA Vincent, H Nishimura, M Itkina, M Schwager First Workshop on Out-of-Distribution Generalization in Robotics at CoRL 2023, 2023 | | 2023 |
| Predicting Running Injuries with Classification Machine Learning Models E Vuong, J Vincent Journal of Student Research 12 (1), 2023 | | 2023 |
Mac SchwagerStanford UniversityVerified email at stanford.edu
