54 citations · 86 across the 11 of their papers we have counts for
9 papers · 1 filter
Sound Probabilistic Inference via Guide Types
Di Wang, Jan Hoffmann, Thomas Reps
Probabilistic programming languages aim to describe and automate Bayesian modeling and inference. Modern languages support programmable inference, which allows users to customize i…
Expected-Cost Analysis for Probabilistic Programs and Semantics-Level Adaption of Optional Stopping Theorems
Di Wang, Jan Hoffmann, Thomas Reps
In this article, we present a semantics-level adaption of the Optional Stopping Theorem, sketch an expected-cost analysis as its application, and survey different variants of the O…
Synthesis with Asymptotic Resource Bounds
Qinheping Hu, John Cyphert, Loris D'Antoni +1
We present a method for synthesizing recursive functions that satisfy both a functional specification and an asymptotic resource bound. Prior methods for synthesis with a resource…
Semantics-Guided Synthesis
Jinwoo Kim, Qinheping Hu, Loris D'Antoni +1
This paper develops a new framework for program synthesis, called semantics-guided synthesis (SemGuS), that allows a user to provide both the syntax and the semantics for the const…
A Generating-Extension-Generator for Machine Code
Michael Vaughn, Thomas Reps
The problem of "debloating" programs for security and performance purposes has begun to see increased attention. Of particular interest in many environments is debloating commodity…
Exact and Approximate Methods for Proving Unrealizability of Syntax-Guided Synthesis Problems
Qinheping Hu, John Cyphert, Loris D'Antoni +1
We consider the problem of automatically establishing that a given syntax-guided-synthesis (SyGuS) problem is unrealizable (i.e., has no solution). We formulate the problem of prov…