[Ml-stat-talks] Fwd: 4/25/2017 Swarat Chaudhuri: Learning to Write Code, Automatically

Barbara Engelhardt bee at princeton.edu
Sun Apr 23 19:10:17 EDT 2017


Talk of interest.

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Hi all,
Swarat Chaudhuri is visiting tomorrow (April 24th).  He'll be talking about
using statistical machine learning for program synthesis at 1:30 in room
302.
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*Learning to Write Code, Automatically*






* Speaker: Swarat Chaudhuri, Rice University (Based on joint work with
Vijay Murali and Chris Jermaine) Automating computer programming is a
long-standing goal in computer science. In spite of significant progress in
automated program synthesis in recent times, we remain very far from
achieving this goal. Indeed, on almost all everyday programming tasks, a
freshman CS major would perform vastly better than today's best program
synthesizers. Two critical components of the gap between human programmers
and program synthesizers are that humans learn from experience, i.e., data,
and can easily generalize from incomplete problem definitions. In this
talk, I will present a new framework for program synthesis, based on
Bayesian statistical learning, that aims to eliminate these differences. In
our framework, the description of a programming task is seen to consist of
a set of "clues" towards a hidden (probabilistic) specification that fully
defines the task. Large corpora of real-world programs are used to
construct a statistical model that correlates specifications with the form
and function of their implementations. The framework can be implemented in
a variety of ways, but in particular, through a neural architecture called
variational Bayesian encoder-decoders. Inferences made using the framework
can be used to guide algorithms for synthesis that search a combinatorial
space of programs. I will show that this data-driven approach can lead to
giant leaps in the scope and performance of automated program synthesis.
Specifically, I will give a demo of Bayou, a system for Bayesian synthesis
of Java programs that goes significantly beyond the state of the art in
program synthesis. I will also show that our framework has applications
beyond synthesis -- in particular, that it can be used to find bugs in
programs without any kind of formal correctness specification. Bio: Swarat
Chaudhuri is an Associate Professor of Computer Science at Rice University.
His research interests span formal methods, programming systems, and
increasingly, the interface of these areas with artificial intelligence. *



-- 
Barbara E Engelhardt
Assistant Professor
Department of Computer Science
Center for Statistics and Machine Learning
Princeton University
http://www.cs.princeton.edu/~bee
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