An up-to-date list may be found at Google Scholar

  • 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 Hewitt, Armando Solar-Lezama, Josh Tenenbaum.
    arxiv preprint

  • Learning Compositional Rules via Neural Program Synthesis.
    Maxwell Nye, Armando Solar-Lezama, Josh Tenenbaum, Brenden Lake.
    NeurIPS 2020. paper code
    Also presented at NeurIPS 2019 Workshop on Context and Compositionality as a spotlight talk.

  • Write, Execute, Assess: Program Synthesis with a REPL.
    Kevin Ellis*, Maxwell Nye*, Yewen Pu*, Felix Sosa*, Josh Tenenbaum, Armando Solar-Lezama.
    NeurIPS 2019. paper

  • Learning to Infer Program Sketches.
    Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama.
    ICML 2019. paper code
    Press: MIT News

  • The Variational Homoencoder: Learning to Infer High-Capacity Generative Models from Few Examples.
    Luke Hewitt, Maxwell Nye, Andreea Gane, Tommi Jaakkola, Josh Tenenbaum.
    UAI 2018, oral presentation. paper code

  • Are Efficient Deep Representations Learnable?
    Maxwell Nye, Andrew Saxe.
    ICLR 2018, workshop. paper