| Reluplex: An efficient SMT solver for verifying deep neural networks G Katz, C Barrett, DL Dill, K Julian, MJ Kochenderfer Computer Aided Verification: 29th International Conference, CAV 2017 …, 2017 | 2293 | 2017 |
| Cooperative multi-agent control using deep reinforcement learning JK Gupta, M Egorov, M Kochenderfer Autonomous Agents and Multiagent Systems: AAMAS 2017 Workshops, Best Papers …, 2017 | 1187 | 2017 |
| Decision making under uncertainty: theory and application MJ Kochenderfer MIT Press, 2015 | 798 | 2015 |
| Algorithms for Optimization MJ Kochenderfer, TA Wheeler MIT Press, 2019 | 751 | 2019 |
| The marabou framework for verification and analysis of deep neural networks G Katz, DA Huang, D Ibeling, K Julian, C Lazarus, R Lim, P Shah, ... Computer Aided Verification: 31st International Conference, CAV 2019, New …, 2019 | 633 | 2019 |
| Imitating driver behavior with generative adversarial networks A Kuefler, J Morton, T Wheeler, M Kochenderfer 2017 IEEE intelligent vehicles symposium (IV), 204-211, 2017 | 539 | 2017 |
| Algorithms for verifying deep neural networks C Liu, T Arnon, C Lazarus, C Barrett, MJ Kochenderfer arXiv preprint arXiv:1903.06758, 2019 | 447 | 2019 |
| Policy compression for aircraft collision avoidance systems KD Julian, J Lopez, JS Brush, MP Owen, MJ Kochenderfer 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), 1-10, 2016 | 307 | 2016 |
| Analysis of recurrent neural networks for probabilistic modeling of driver behavior J Morton, TA Wheeler, MJ Kochenderfer IEEE Transactions on Intelligent Transportation Systems 18 (5), 1289-1298, 2016 | 290 | 2016 |
| Combining planning and deep reinforcement learning in tactical decision making for autonomous driving CJ Hoel, K Driggs-Campbell, K Wolff, L Laine, MJ Kochenderfer IEEE transactions on intelligent vehicles 5 (2), 294-305, 2019 | 282 | 2019 |
| Next generation airborne collision avoidance system MJ Kochenderfer, JE Holland, JP Chryssanthacopoulos Lincoln Laboratory Journal 19 (1), 17-33, 2012 | 280 | 2012 |
| Online algorithms for POMDPs with continuous state, action, and observation spaces Z Sunberg, M Kochenderfer Proceedings of the International Conference on Automated Planning and …, 2018 | 275 | 2018 |
| Learning near optimal policies with low inherent bellman error A Zanette, A Lazaric, M Kochenderfer, E Brunskill International Conference on Machine Learning, 10978-10989, 2020 | 240 | 2020 |
| Generalizable intention prediction of human drivers at intersections DJ Phillips, TA Wheeler, MJ Kochenderfer 2017 IEEE intelligent vehicles symposium (IV), 1665-1670, 2017 | 225 | 2017 |
| Adaptive stress testing for autonomous vehicles M Koren, S Alsaif, R Lee, MJ Kochenderfer 2018 IEEE Intelligent Vehicles Symposium (IV), 1-7, 2018 | 223 | 2018 |
| Deep neural network compression for aircraft collision avoidance systems KD Julian, MJ Kochenderfer, MP Owen Journal of Guidance, Control, and Dynamics 42 (3), 598-608, 2019 | 216 | 2019 |
| Algorithms for decision making MJ Kochenderfer, TA Wheeler, KH Wray MIT press, 2022 | 201 | 2022 |
| Collision avoidance for unmanned aircraft using Markov decision processes S Temizer, M Kochenderfer, L Kaelbling, T Lozano-Pérez, J Kuchar AIAA guidance, navigation, and control conference, 8040, 2010 | 199 | 2010 |
| A survey of algorithms for black-box safety validation of cyber-physical systems A Corso, R Moss, M Koren, R Lee, M Kochenderfer Journal of Artificial Intelligence Research 72, 377–428-377–428, 2021 | 195 | 2021 |
| Hg-dagger: Interactive imitation learning with human experts M Kelly, C Sidrane, K Driggs-Campbell, MJ Kochenderfer 2019 International Conference on Robotics and Automation (ICRA), 8077-8083, 2019 | 194 | 2019 |
Kyle David JulianReceived Ph.D. at Stanford UniversityVerified email at stanford.edu
