| Language models can teach themselves to program better P Haluptzok, M Bowers, AT Kalai arXiv preprint arXiv:2207.14502, 2022 | 62 | 2022 |
| Top-Down Synthesis For Library Learning M Bowers, TX Olausson, C Wong, G Grand, JB Tenenbaum, K Ellis, ... Proceedings of the ACM on Programming Languages - POPL 7, 2023 | 55 | 2023 |
| Representing partial programs with blended abstract semantics M Nye, Y Pu, M Bowers, J Andreas, JB Tenenbaum, A Solar-Lezama arXiv preprint arXiv:2012.12964, 2020 | 25 | 2020 |
| Universal reshaping of arrested colloidal gels via active doping SA Mallory, ML Bowers, A Cacciuto The Journal of Chemical Physics 153 (8), 2020 | 15 | 2020 |
| Lilo: Learning interpretable libraries by compressing and documenting code G Grand, L Wong, M Bowers, TX Olausson, M Liu, JB Tenenbaum, ... arXiv preprint arXiv:2310.19791, 2023 | 13 | 2023 |
| Active sculpting of colloidal crystals S Das, M Lee Bowers, C Bakker, A Cacciuto The Journal of chemical physics 150 (13), 2019 | 9 | 2019 |
| Learning Interpretable Libraries by Compressing and Documenting Code G Grand, L Wong, M Bowers, TX Olausson, M Liu, JB Tenenbaum, ... Intrinsically-Motivated and Open-Ended Learning Workshop@ NeurIPS2023, 2023 | 2 | 2023 |
| Generating Programming Puzzles to Train Language Models P Haluptzok, M Bowers, AT Kalai Deep Learning for Code Workshop, 0 | 1 | |
| MathDSL: A Domain-Specific Language for Concise Mathematical Solutions Via Program Synthesis S Anupam, M Bowers, O Costilla-Reyes, A Solar-Lezama arXiv preprint arXiv:2409.17490, 2024 | | 2024 |
| Concept Learning as Coarse-to-Fine Probabilistic Program Induction ML Bowers, A Lew, W Qi, JS Rule, V Mansinghka, J Tenenbaum, ... Proceedings of the Annual Meeting of the Cognitive Science Society 46, 2024 | | 2024 |
| Codeplay: Autotelic Learning through Collaborative Self-Play in Programming Environments L Teodorescu, C Colas, M Bowers, T Carta, PY Oudeyer IMOL 2023-Intrinsically Motivated Open-ended Learning workshop at NeurIPS 2023, 2023 | | 2023 |