| Learning to infer graphics programs from hand-drawn images K Ellis, D Ritchie, A Solar-Lezama, J Tenenbaum Advances in neural information processing systems 31, 2018 | 262 | 2018 |
| DreamCoder: growing generalizable, interpretable knowledge with wake–sleep Bayesian program learning K Ellis, L Wong, M Nye, M Sable-Meyer, L Cary, L Anaya Pozo, L Hewitt, ... Philosophical Transactions of the Royal Society A 381 (2251), 20220050, 2023 | 229 | 2023 |
| Dreamcoder: Bootstrapping inductive program synthesis with wake-sleep library learning K Ellis, C Wong, M Nye, M Sablé-Meyer, L Morales, L Hewitt, L Cary, ... Proceedings of the 42nd acm sigplan international conference on programming …, 2021 | 180 | 2021 |
| Learning to infer and execute 3d shape programs Y Tian, A Luo, X Sun, K Ellis, WT Freeman, JB Tenenbaum, J Wu arXiv preprint arXiv:1901.02875, 2019 | 162 | 2019 |
| Write, execute, assess: Program synthesis with a repl K Ellis, M Nye, Y Pu, F Sosa, J Tenenbaum, A Solar-Lezama Advances in Neural Information Processing Systems 32, 2019 | 161 | 2019 |
| Learning libraries of subroutines for neurally–guided bayesian program induction K Ellis, L Morales, M Sablé-Meyer, A Solar-Lezama, J Tenenbaum Advances in Neural Information Processing Systems 31, 2018 | 111 | 2018 |
| Bias reformulation for one-shot function induction D Lin, E Dechter, K Ellis, J Tenenbaum, S Muggleton ECAI 2014, 525-530, 2014 | 111 | 2014 |
| Unsupervised learning by program synthesis K Ellis, A Solar-Lezama, J Tenenbaum Advances in neural information processing systems 28, 2015 | 96 | 2015 |
| 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 | 89 | 2021 |
| A language of thought for the mental representation of geometric shapes M Sablé-Meyer, K Ellis, J Tenenbaum, S Dehaene Cognitive Psychology 139, 101527, 2022 | 83 | 2022 |
| Leveraging language to learn program abstractions and search heuristics C Wong, KM Ellis, J Tenenbaum, J Andreas International conference on machine learning, 11193-11204, 2021 | 58 | 2021 |
| Top-down synthesis for library learning M Bowers, TX Olausson, L Wong, G Grand, JB Tenenbaum, K Ellis, ... Proceedings of the ACM on Programming Languages 7 (POPL), 1182-1213, 2023 | 49 | 2023 |
| Sampling for bayesian program learning K Ellis, A Solar-Lezama, J Tenenbaum Advances in Neural Information Processing Systems 29, 2016 | 43 | 2016 |
| Making sense of raw input R Evans, M Bošnjak, L Buesing, K Ellis, D Pfau, P Kohli, M Sergot Artificial Intelligence 299, 103521, 2021 | 38 | 2021 |
| Learning to Learn Programs from Examples: Going Beyond Program Structure. K Ellis, S Gulwani IJCAI, 1638-1645, 2017 | 38 | 2017 |
| Learning abstract structure for drawing by efficient motor program induction L Tian, K Ellis, M Kryven, J Tenenbaum Advances in Neural Information Processing Systems 33, 2686-2697, 2020 | 35 | 2020 |
| Synthesizing theories of human language with Bayesian program induction K Ellis, A Albright, A Solar-Lezama, JB Tenenbaum, TJ O’Donnell Nature communications 13 (1), 5024, 2022 | 27 | 2022 |
| CrossBeam: Learning to search in bottom-up program synthesis K Shi, H Dai, K Ellis, C Sutton arXiv preprint arXiv:2203.10452, 2022 | 26 | 2022 |
| Program synthesis with pragmatic communication Y Pu, K Ellis, M Kryven, J Tenenbaum, A Solar-Lezama Advances in neural information processing systems 33, 13249-13259, 2020 | 23 | 2020 |
| Toward trustworthy neural program synthesis D Key, WD Li, K Ellis arXiv preprint arXiv:2210.00848, 2022 | 18* | 2022 |
