| Syntax-guided synthesis R Alur, R Bodik, G Juniwal, MMK Martin, M Raghothaman, SA Seshia, ... 2013 Formal Methods in Computer-Aided Design, 1-8, 2013 | 734 | 2013 |
| Automated feedback generation for introductory programming assignments R Singh, S Gulwani, A Solar-Lezama Proceedings of the 34th SIGPLAN conference on Programming Language Design …, 2013 | 497 | 2013 |
| Program synthesis S Gulwani, O Polozov, R Singh Foundations and Trends® in Programming Languages 4 (1-2), 1-119, 2017 | 403 | 2017 |
| Automated correction for syntax errors in programming assignments using recurrent neural networks S Bhatia, R Singh arXiv preprint arXiv:1603.06129, 2016 | 348 | 2016 |
| Robustfill: Neural program learning under noisy i/o J Devlin, J Uesato, S Bhupatiraju, R Singh, A Mohamed, P Kohli International conference on machine learning, 990-998, 2017 | 334 | 2017 |
| Spreadsheet data manipulation using examples S Gulwani, WR Harris, R Singh Communications of the ACM 55 (8), 97-105, 2012 | 328 | 2012 |
| Learn&fuzz: Machine learning for input fuzzing P Godefroid, H Peleg, R Singh 2017 32nd IEEE/ACM International Conference on Automated Software …, 2017 | 322 | 2017 |
| Neuro-symbolic program synthesis E Parisotto, A Mohamed, R Singh, L Li, D Zhou, P Kohli arXiv preprint arXiv:1611.01855, 2016 | 300 | 2016 |
| Programmatically interpretable reinforcement learning A Verma, V Murali, R Singh, P Kohli, S Chaudhuri International Conference on Machine Learning, 5045-5054, 2018 | 247 | 2018 |
| Leveraging grammar and reinforcement learning for neural program synthesis R Bunel, M Hausknecht, J Devlin, R Singh, P Kohli arXiv preprint arXiv:1805.04276, 2018 | 161 | 2018 |
| OverCode: Visualizing variation in student solutions to programming problems at scale EL Glassman, J Scott, R Singh, PJ Guo, RC Miller ACM Transactions on Computer-Human Interaction (TOCHI) 22 (2), 1-35, 2015 | 156 | 2015 |
| Learning semantic string transformations from examples R Singh, S Gulwani arXiv preprint arXiv:1204.6079, 2012 | 147 | 2012 |
| Global relational models of source code VJ Hellendoorn, C Sutton, R Singh, P Maniatis, D Bieber International conference on learning representations, 2019 | 131 | 2019 |
| Synthesizing number transformations from input-output examples R Singh, S Gulwani International Conference on Computer Aided Verification, 634-651, 2012 | 129 | 2012 |
| Qlose: Program repair with quantitative objectives L D’Antoni, R Samanta, R Singh International Conference on Computer Aided Verification, 383-401, 2016 | 121 | 2016 |
| Dynamic neural program embedding for program repair K Wang, R Singh, Z Su arXiv preprint arXiv:1711.07163, 2017 | 120 | 2017 |
| Terpret: A probabilistic programming language for program induction AL Gaunt, M Brockschmidt, R Singh, N Kushman, P Kohli, J Taylor, ... arXiv preprint arXiv:1608.04428, 2016 | 117 | 2016 |
| Natural language to structured query generation via meta-learning PS Huang, C Wang, R Singh, W Yih, X He arXiv preprint arXiv:1803.02400, 2018 | 108 | 2018 |
| Blinkfill: Semi-supervised programming by example for syntactic string transformations R Singh Proceedings of the VLDB Endowment 9 (10), 816-827, 2016 | 104 | 2016 |
| Synthesizing data structure manipulations from storyboards R Singh, A Solar-Lezama Proceedings of the 19th ACM SIGSOFT symposium and the 13th European …, 2011 | 104 | 2011 |
Sumit GulwaniPartner Research Manager, MicrosoftVerified email at microsoft.com
