In this paper we introduce a new approach for learning precise and general probabilistic
models of code based on decision tree learning. Our approach directly benefits an emerging …

We present a method for synthesizing recursive functions that provably satisfy a given
specification in the form of a polymorphic refinement type. We observe that such …

This paper presents an algorithm for synthesizing recursive functions that process algebraic
datatypes. It is founded on proof-theoretic techniques that exploit both type information and …

Given a semantic constraint specified by a logical formula, and a syntactic constraint
specified by a context-free grammar, the Syntax-Guided Synthesis (SyGuS) problem is to …

Modern program synthesizers are increasingly delivering on their promise of lightening the
burden of programming by automatically generating code, but little research has addressed …

We introduce the first program synthesis engine implemented inside an SMT solver. We
present an approach that extracts solution functions from unsatisfiability proofs of the …

Object detection is always conducted by object proposal generation and classification
sequentially. This paper handles object detection in a superpixel oriented manner instead of …

By abstracting away the complexity of distributed systems, large-scale data processing
platforms—MapReduce, Hadoop, Spark, Dryad, etc.—have provided developers with simple …

We propose a novel trace-guided approach to tackle the challenges of ambiguity and
generalization in synthesis of recursive functional programs from input-output examples. Our …

We present a novel bottom-up method for the synthesis of functional recursive programs.
While bottom-up synthesis techniques can work better than top-down methods in certain …