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Workshop
Workshop on Computer Assisted Programming (CAP)
Augustus Odena · Charles Sutton · Nadia Polikarpova · Josh Tenenbaum · Armando Solar-Lezama · Isil Dillig

Sat Dec 12 08:30 AM -- 04:10 PM (PST) @
in Workshops Sat »
Event URL: https://capworkshop.github.io/ »

There are many tasks that could be automated by writing computer programs, but most people don’t know how to program computers (this is the subject of program synthesis, the study of how to automatically write programs from user specifications). Building tools for doing computer-assisted-programming could thus improve the lives of many people (and it’s also a cool research problem!). There has been substantial recent interest in the ML community in the problem of automatically writing computer programs from user specifications, as evidenced by the increased volume of Program Synthesis submissions to ICML, ICLR, and NeurIPS.

Despite this recent work, a lot of exciting questions are still open, such as how to combine symbolic reasoning over programs with deep learning, how to represent programs and user specifications, and how to apply program synthesis within computer vision, robotics, and other control problems. There is also work to be done on fusing work done in the ML community with research on Programming Languages (PL) through collaboration between the ML and PL communities, and there remains the challenge of establishing benchmarks that allow for easy comparison and measurement of progress. The aim of the CAP workshop is to address these points. This workshop will bring together researchers in programming languages, machine learning, and related areas who are interested in program synthesis and other methods for automatically writing programs from a specification of intended behavior.

Author Information

Augustus Odena (Google Brain)
Charles Sutton (Google)
Nadia Polikarpova (University of California, San Diego)
Josh Tenenbaum (MIT)

Josh Tenenbaum is an Associate Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 1999, and was an Assistant Professor at Stanford University from 1999 to 2002. He studies learning and inference in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities. He focuses on problems of inductive generalization from limited data -- learning concepts and word meanings, inferring causal relations or goals -- and learning abstract knowledge that supports these inductive leaps in the form of probabilistic generative models or 'intuitive theories'. He has also developed several novel machine learning methods inspired by human learning and perception, most notably Isomap, an approach to unsupervised learning of nonlinear manifolds in high-dimensional data. He has been Associate Editor for the journal Cognitive Science, has been active on program committees for the CogSci and NIPS conferences, and has co-organized a number of workshops, tutorials and summer schools in human and machine learning. Several of his papers have received outstanding paper awards or best student paper awards at the IEEE Computer Vision and Pattern Recognition (CVPR), NIPS, and Cognitive Science conferences. He is the recipient of the New Investigator Award from the Society for Mathematical Psychology (2005), the Early Investigator Award from the Society of Experimental Psychologists (2007), and the Distinguished Scientific Award for Early Career Contribution to Psychology (in the area of cognition and human learning) from the American Psychological Association (2008).

Armando Solar-Lezama (MIT)
Isil Dillig (UT Austin)

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