A study of android application security. W Enck, D Octeau, PD McDaniel, S Chaudhuri USENIX security symposium 2 (2), 2011 | 1388 | 2011 |
Ai2: Safety and robustness certification of neural networks with abstract interpretation T Gehr, M Mirman, D Drachsler-Cohen, P Tsankov, S Chaudhuri, ... 2018 IEEE symposium on security and privacy (SP), 3-18, 2018 | 1014 | 2018 |
Programmatically Interpretable Reinforcement Learning A Verma, V Murali, R Singh, P Kohli, S Chaudhuri Proceedings of the 35th International Conference on Machine Learning (ICML …, 2018 | 415 | 2018 |
Synthesizing data structure transformations from input-output examples JK Feser, S Chaudhuri, I Dillig Symposium on Programming Language Design and Implementation (PLDI), 229-239, 2015 | 393 | 2015 |
Incremental task and motion planning: A constraint-based approach. NT Dantam, ZK Kingston, S Chaudhuri, LE Kavraki Robotics: Science and systems 12, 00052, 2016 | 236 | 2016 |
Component-based synthesis of table consolidation and transformation tasks from examples Y Feng, R Martins, J Van Geffen, I Dillig, S Chaudhuri ACM SIGPLAN Notices 52 (6), 422-436, 2017 | 214 | 2017 |
Neural Sketch Learning for Conditional Program Generation V Murali, L Qi, S Chaudhuri, C Jermaine International Conference for Learning Representations (ICLR), 2018 | 178* | 2018 |
Proving programs robust S Chaudhuri, S Gulwani, R Lublinerman, S Navidpour Proceedings of the 19th ACM SIGSOFT symposium and the 13th European …, 2011 | 171 | 2011 |
An incremental constraint-based framework for task and motion planning NT Dantam, ZK Kingston, S Chaudhuri, LE Kavraki The International Journal of Robotics Research 37 (10), 1134-1151, 2018 | 160 | 2018 |
Continuity analysis of programs S Chaudhuri, S Gulwani, R Lublinerman 37th Symposium on Principles of Programming Languages (POPL), 57-70, 2010 | 127 | 2010 |
Optimization and abstraction: a synergistic approach for analyzing neural network robustness G Anderson, S Pailoor, I Dillig, S Chaudhuri Proceedings of the 40th ACM SIGPLAN conference on programming language …, 2019 | 114 | 2019 |
A constraint-based approach to solving games on infinite graphs T Beyene, S Chaudhuri, C Popeea, A Rybalchenko Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of …, 2014 | 111 | 2014 |
SMT-based synthesis of integrated task and motion plans from plan outlines S Nedunuri, S Prabhu, M Moll, S Chaudhuri, LE Kavraki 2014 IEEE International Conference on Robotics and Automation (ICRA), 655-662, 2014 | 108 | 2014 |
Subcubic algorithms for recursive state machines S Chaudhuri Symposium on Principles of Programming Languages (POPL), 159-169, 2008 | 101 | 2008 |
Imitation-projected programmatic reinforcement learning A Verma, HM Le, Y Yue, S Chaudhuri Neural Information Processing Systems (NeurIPS), 15726--15737, 2019 | 100 | 2019 |
Houdini: Lifelong learning as program synthesis L Valkov, D Chaudhari, A Srivastava, C Sutton, S Chaudhuri Neural Information Processing Systems (NeurIPS), 2018 | 95 | 2018 |
Control regularization for reduced variance reinforcement learning R Cheng, A Verma, G Orosz, S Chaudhuri, Y Yue, J Burdick International Conference on Machine Learning, 1141-1150, 2019 | 89 | 2019 |
Continuity and robustness of programs S Chaudhuri, S Gulwani, R Lublinerman Communications of the ACM 55 (8), 107-115, 2012 | 88 | 2012 |
Neurosymbolic programming S Chaudhuri, K Ellis, O Polozov, R Singh, A Solar-Lezama, Y Yue Foundations and Trends® in Programming Languages 7 (3), 158-243, 2021 | 82 | 2021 |
Neurosymbolic Reinforcement Learning with Formally Verified Exploration G Anderson, A Verma, I Dillig, S Chaudhuri Neural Information Processing Systems (NeurIPS), 2020 | 82 | 2020 |