Top-Down Synthesis for Library Learning

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Top-Down Synthesis for Library Learning

29 November 2022

Theo X. Olausson

Armando Solar-Lezama

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Papers citing "Top-Down Synthesis for Library Learning"

9 / 9 papers shown

Title
Common Benchmarks Undervalue the Generalization Power of Programmatic Policies Amirhossein Rajabpour Kiarash Aghakasiri Sandra Zilles Levi H. S. Lelis OffRL 28 0 0 17 Jun 2025
Refactoring Codebases through Library Design Ziga Kovacic Celine Lee Justin T Chiu Wenting Zhao Kevin Ellis 19 0 0 26 May 2025
MeMo: Meaningful, Modular Controllers via Noise Injection Megan Tjandrasuwita Jie Xu Armando Solar-Lezama Wojciech Matusik 97 0 0 24 May 2024
REFACTOR: Learning to Extract Theorems from Proofs Jin Peng Zhou Yuhuai Wu Qiyang Li Roger C. Grosse AIMat 79 8 0 26 Feb 2024
ShapeCoder: Discovering Abstractions for Visual Programs from Unstructured Primitives R. K. Jones Paul Guerrero Niloy J. Mitra Daniel E. Ritchie 77 25 0 09 May 2023
Programming-by-Demonstration for Long-Horizon Robot Tasks Noah T Patton Kia Rahmani Meghana Missula Joydeep Biswas Icsil Dillig 96 12 0 04 May 2023
Anti-unification and Generalization: A Survey David M. Cerna Temur Kutsia AI4CE 91 17 0 01 Feb 2023
Neurosymbolic Programming for Science Jennifer J. Sun Megan Tjandrasuwita Atharva Sehgal Armando Solar-Lezama Swarat Chaudhuri Yisong Yue Omar Costilla-Reyes NAI 101 12 0 10 Oct 2022
DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning Kevin Ellis Catherine Wong Maxwell Nye Mathias Sablé-Meyer Luc Cary Lucas Morales Luke B. Hewitt Armando Solar-Lezama J. Tenenbaum NAI CLL 114 196 0 15 Jun 2020