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20122024
most citedQuantifying Program Bias

10 citations · 16 across the 7 of their papers we have counts for

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Showing cs.PLShow all

8 papers · 1 filter

cs.PL2021

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…

cs.PL2020

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…

cs.PL2020

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…

cs.PL2019

Proving Data-Poisoning Robustness in Decision Trees

Samuel Drews, Aws Albarghouthi, Loris D'Antoni

Machine learning models are brittle, and small changes in the training data can result in different predictions. We study the problem of proving that a prediction is robust to data…

cs.PL2019

Efficient Synthesis with Probabilistic Constraints

Samuel Drews, Aws Albarghouthi, Loris D'Antoni

We consider the problem of synthesizing a program given a probabilistic specification of its desired behavior. Specifically, we study the recent paradigm of distribution-guided ind…

cs.PL2019

Proving Unrealizability for Syntax-Guided Synthesis

Qinheping Hu, Jason Breck, John Cyphert +2

Proving Unrealizability for Syntax-Guided Synthesis We consider the problem of automatically establishing that a given syntax-guided-synthesis (SyGuS) problem is unrealizable (i.e.…