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Top-Down Synthesis for Library Learning., , , , , , and . Proc. ACM Program. Lang., 7 (POPL): 1182-1213 (January 2023)Learning to Learn Programs from Examples: Going Beyond Program Structure., and . IJCAI, page 1638-1645. ijcai.org, (2017)DeepSynth: Scaling Neural Program Synthesis with Distribution-based Search., , , and . J. Open Source Softw., 7 (78): 4151 (October 2022)Neurosymbolic Programming., , , , , and . Found. Trends Program. Lang., 7 (3): 158-243 (2021)Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning., and . CoRR, (2024)I Speak, You Verify: Toward Trustworthy Neural Program Synthesis., , and . CoRR, (2022)Scaling Neural Program Synthesis with Distribution-Based Search., , , , , and . AAAI, page 6623-6630. AAAI Press, (2022)Leveraging Language to Learn Program Abstractions and Search Heuristics., , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 11193-11204. PMLR, (2021)From Perception to Programs: Regularize, Overparameterize, and Amortize., and . ICML, volume 202 of Proceedings of Machine Learning Research, page 33616-33631. PMLR, (2023)Learning Libraries of Subroutines for Neurally-Guided Bayesian Program Induction., , , , and . NeurIPS, page 7816-7826. (2018)