| dReal: An SMT Solver for Nonlinear Theories over the Reals S Gao, S Kong, EM Clarke Automated Deduction–CADE-24: 24th International Conference on Automated …, 2013 | 482 | 2013 |
| dReach: δ-Reachability Analysis for Hybrid Systems S Kong, S Gao, W Chen, E Clarke Tools and Algorithms for the Construction and Analysis of Systems: 21st …, 2015 | 311 | 2015 |
| δ-Complete Decision Procedures for Satisfiability over the Reals S Gao, J Avigad, EM Clarke Automated Reasoning: 6th International Joint Conference, IJCAR 2012 …, 2012 | 205 | 2012 |
| Neural lyapunov control YC Chang, N Roohi, S Gao Advances in neural information processing systems 32, 2019 | 204 | 2019 |
| Satisfiability modulo odes S Gao, S Kong, EM Clarke 2013 Formal Methods in Computer-Aided Design, 105-112, 2013 | 112 | 2013 |
| A Non-prenex, Non-clausal QBF Solver with Game-State Learning. W Klieber, S Sapra, S Gao, EM Clarke SAT 6175, 128-142, 2010 | 94 | 2010 |
| SMT-based nonlinear PDDL+ planning D Bryce, S Gao, D Musliner, R Goldman Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 91 | 2015 |
| Delta-decidability over the reals S Gao, J Avigad, EM Clarke 2012 27th Annual IEEE Symposium on Logic in Computer Science, 305-314, 2012 | 90 | 2012 |
| Counting zeros over finite fields with Gröbner bases S Gao Master’s thesis, Carnegie Mellon University, 2009 | 57* | 2009 |
| Integrating ICP and LRA solvers for deciding nonlinear real arithmetic problems S Gao, M Ganai, F Ivančić, A Gupta, S Sankaranarayanan, EM Clarke Formal Methods in Computer Aided Design, 81-89, 2010 | 55 | 2010 |
| Safe nonlinear control using robust neural lyapunov-barrier functions C Dawson, Z Qin, S Gao, C Fan Conference on Robot Learning, 1724-1735, 2022 | 53 | 2022 |
| APEX: Autonomous vehicle plan verification and execution M O'Kelly, H Abbas, S Gao, S Shiraishi, S Kato, R Mangharam | 36 | 2016 |
| Safe control with learned certificates: A survey of neural lyapunov, barrier, and contraction methods C Dawson, S Gao, C Fan arXiv preprint arXiv:2202.11762, 2022 | 35 | 2022 |
| How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies? Q Vuong, S Vikram, H Su, S Gao, HI Christensen arXiv preprint arXiv:1903.11774, 2019 | 32 | 2019 |
| Releq: an automatic reinforcement learning approach for deep quantization of neural networks A Elthakeb, P Pilligundla, FS Mireshghallah, A Yazdanbakhsh, S Gao, ... NeurIPS ML for Systems workshop, 2018, 2019 | 31 | 2019 |
| Delta-complete analysis for bounded reachability of hybrid systems S Gao, S Kong, W Chen, E Clarke arXiv preprint arXiv:1404.7171, 2014 | 30 | 2014 |
| Sreach: A probabilistic bounded delta-reachability analyzer for stochastic hybrid systems Q Wang, P Zuliani, S Kong, S Gao, EM Clarke Computational Methods in Systems Biology: 13th International Conference …, 2015 | 29* | 2015 |
| Stabilizing neural control using self-learned almost Lyapunov critics YC Chang, S Gao 2021 IEEE International Conference on Robotics and Automation (ICRA), 1803-1809, 2021 | 26 | 2021 |
| Parameter Synthesis for Cardiac Cell Hybrid Models Using δ-Decisions B Liu, S Kong, S Gao, P Zuliani, EM Clarke Computational Methods in Systems Biology: 12th International Conference …, 2014 | 23* | 2014 |
| A neural lyapunov approach to transient stability assessment of power electronics-interfaced networked microgrids T Huang, S Gao, L Xie IEEE Transactions on Smart Grid 13 (1), 106-118, 2021 | 22 | 2021 |
