| PaLM: Scaling Language Modeling with Pathways A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ... Journal of Machine Learning Research 24 (240), 1-113, 2023 | 3344 | 2023 |
| Learning and Evaluating Contextual Embedding of Source Code A Kanade, P Maniatis, G Balakrishnan, K Shi International Conference on Machine Learning, 5110-5121, 2020 | 376 | 2020 |
| FrAngel: component-based synthesis with control structures K Shi, J Steinhardt, P Liang Proceedings of the ACM on Programming Languages 3 (POPL), 73, 2019 | 53 | 2019 |
| Spark PRM: Using RRTs within PRMs to efficiently explore narrow passages K Shi, J Denny, NM Amato 2014 IEEE International Conference on Robotics and Automation (ICRA), 4659-4666, 2014 | 45 | 2014 |
| BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration A Odena, K Shi, D Bieber, R Singh, C Sutton, H Dai International Conference on Learning Representations, 2021 | 43 | 2021 |
| Lazy Toggle PRM: A single-query approach to motion planning J Denny, K Shi, NM Amato 2013 IEEE International Conference on Robotics and Automation, 2407-2414, 2013 | 39 | 2013 |
| TF-Coder: Program synthesis for tensor manipulations K Shi, D Bieber, R Singh ACM Transactions on Programming Languages and Systems (TOPLAS) 44 (2), 1-36, 2022 | 33 | 2022 |
| Can large language models reason about program invariants? K Pei, D Bieber, K Shi, C Sutton, P Yin International Conference on Machine Learning, 27496-27520, 2023 | 26 | 2023 |
| Natural language to code generation in interactive data science notebooks P Yin, WD Li, K Xiao, A Rao, Y Wen, K Shi, J Howland, P Bailey, ... arXiv preprint arXiv:2212.09248, 2022 | 26 | 2022 |
| CrossBeam: Learning to Search in Bottom-Up Program Synthesis K Shi, H Dai, K Ellis, C Sutton International Conference on Learning Representations, 2021 | 18 | 2021 |
| Incremental sampling without replacement for sequence models K Shi, D Bieber, C Sutton International Conference on Machine Learning, 8785-8795, 2020 | 16 | 2020 |
| A Library for Representing Python Programs as Graphs for Machine Learning D Bieber, K Shi, P Maniatis, C Sutton, V Hellendoorn, D Johnson, ... arXiv preprint arXiv:2208.07461, 2022 | 3 | 2022 |
| Systems and methods for synthesizing code from input and output examples K Shi, R Singh, DJ Bieber US Patent 11,256,485, 2022 | 3 | 2022 |
| ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis K Shi, J Hong, M Zaheer, P Yin, C Sutton arXiv preprint arXiv:2307.13883, 2023 | 2 | 2023 |
| Compositional Generalization and Decomposition in Neural Program Synthesis K Shi, J Hong, M Zaheer, P Yin, C Sutton arXiv preprint arXiv:2204.03758, 2022 | 2 | 2022 |
| Grounding Code Generation with Input-Output Specifications Y Wen, P Yin, K Shi, H Michalewski, S Chaudhuri, A Polozov NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following, 2023 | 1 | 2023 |
| LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas K Shi, H Dai, WD Li, K Ellis, C Sutton Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Grounding Data Science Code Generation with Input-Output Specifications Y Wen, P Yin, K Shi, H Michalewski, S Chaudhuri, A Polozov arXiv preprint arXiv:2402.08073, 2024 | | 2024 |
| Graph Representations of Python Programs via Source-level Static Analysis C Sutton, D Johnson, D Tarlow, D Bieber, K Shi, P Maniatis, ... | | 2022 |
Charles SuttonGoogle, University of EdinburghVerified email at inf.ed.ac.uk
