159 citations · 646 across the 48 of their papers we have counts for
14 papers · 1 filter
LEMMA: Bootstrapping High-Level Mathematical Reasoning with Learned Symbolic Abstractions
Zhening Li, Gabriel Poesia, Omar Costilla-Reyes +2
Humans tame the complexity of mathematical reasoning by developing hierarchies of abstractions. With proper abstractions, solutions to hard problems can be expressed concisely, thu…
Neurosymbolic Programming for Science
Jennifer J. Sun, Megan Tjandrasuwita, Atharva Sehgal +4
Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery. These models combine neural and symbolic components to learn complex patterns and r…
Program Synthesis Guided Reinforcement Learning for Partially Observed Environments
Yichen David Yang, Jeevana Priya Inala, Osbert Bastani +3
A key challenge for reinforcement learning is solving long-horizon planning problems. Recent work has leveraged programs to guide reinforcement learning in these settings. However,…
Verifiably Safe Exploration for End-to-End Reinforcement Learning
Nathan Hunt, Nathan Fulton, Sara Magliacane +3
Deploying deep reinforcement learning in safety-critical settings requires developing algorithms that obey hard constraints during exploration. This paper contributes a first appro…
Program Synthesis with Pragmatic Communication
Yewen Pu, Kevin Ellis, Marta Kryven +2
Program synthesis techniques construct or infer programs from user-provided specifications, such as input-output examples. Yet most specifications, especially those given by end-us…
DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
Kevin Ellis, Catherine Wong, Maxwell Nye +6
Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages -- systems of concepts, a…