3 Search Results for "Chen, Sheng"


Document
Almost Optimal Distribution-Free Junta Testing

Authors: Nader H. Bshouty

Published in: LIPIcs, Volume 137, 34th Computational Complexity Conference (CCC 2019)


Abstract
We consider the problem of testing whether an unknown n-variable Boolean function is a k-junta in the distribution-free property testing model, where the distance between functions is measured with respect to an arbitrary and unknown probability distribution over {0,1}^n. Chen, Liu, Servedio, Sheng and Xie [Zhengyang Liu et al., 2018] showed that the distribution-free k-junta testing can be performed, with one-sided error, by an adaptive algorithm that makes O~(k^2)/epsilon queries. In this paper, we give a simple two-sided error adaptive algorithm that makes O~(k/epsilon) queries.

Cite as

Nader H. Bshouty. Almost Optimal Distribution-Free Junta Testing. In 34th Computational Complexity Conference (CCC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 137, pp. 2:1-2:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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@InProceedings{bshouty:LIPIcs.CCC.2019.2,
  author =	{Bshouty, Nader H.},
  title =	{{Almost Optimal Distribution-Free Junta Testing}},
  booktitle =	{34th Computational Complexity Conference (CCC 2019)},
  pages =	{2:1--2:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-116-0},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{137},
  editor =	{Shpilka, Amir},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CCC.2019.2},
  URN =		{urn:nbn:de:0030-drops-108249},
  doi =		{10.4230/LIPIcs.CCC.2019.2},
  annote =	{Keywords: Distribution-free property testing, k-Junta}
}
Document
Blame Tracking and Type Error Debugging

Authors: Sheng Chen and John Peter Campora III

Published in: LIPIcs, Volume 136, 3rd Summit on Advances in Programming Languages (SNAPL 2019)


Abstract
In this work, we present an unexpected connection between gradual typing and type error debugging. Namely, we illustrate that gradual typing provides a natural way to defer type errors in statically ill-typed programs, providing more feedback than traditional approaches to deferring type errors. When evaluating expressions that lead to runtime type errors, the usefulness of the feedback depends on blame tracking, the defacto approach to locating the cause of such runtime type errors. Unfortunately, blame tracking suffers from the bias problem for type error localization in languages with type inference. We illustrate and formalize the bias problem for blame tracking, present ideas for adapting existing type error debugging techniques to combat this bias, and outline further challenges.

Cite as

Sheng Chen and John Peter Campora III. Blame Tracking and Type Error Debugging. In 3rd Summit on Advances in Programming Languages (SNAPL 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 136, pp. 2:1-2:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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@InProceedings{chen_et_al:LIPIcs.SNAPL.2019.2,
  author =	{Chen, Sheng and Campora III, John Peter},
  title =	{{Blame Tracking and Type Error Debugging}},
  booktitle =	{3rd Summit on Advances in Programming Languages (SNAPL 2019)},
  pages =	{2:1--2:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-113-9},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{136},
  editor =	{Lerner, Benjamin S. and Bod{\'\i}k, Rastislav and Krishnamurthi, Shriram},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.SNAPL.2019.2},
  URN =		{urn:nbn:de:0030-drops-105451},
  doi =		{10.4230/LIPIcs.SNAPL.2019.2},
  annote =	{Keywords: Blame tracking, type error debugging, gradual typing, type inference}
}
Document
A Calculus for Variational Programming

Authors: Sheng Chen, Martin Erwig, and Eric Walkingshaw

Published in: LIPIcs, Volume 56, 30th European Conference on Object-Oriented Programming (ECOOP 2016)


Abstract
Variation is ubiquitous in software. Many applications can benefit from making this variation explicit, then manipulating and computing with it directly---a technique we call "variational programming". This idea has been independently discovered in several application domains, such as efficiently analyzing and verifying software product lines, combining bounded and symbolic model-checking, and computing with alternative privacy profiles. Although these domains share similar core problems, and there are also many similarities in the solutions, there is no dedicated programming language support for variational programming. This makes the various implementations tedious, prone to errors, hard to maintain and reuse, and difficult to compare. In this paper we present a calculus that forms the basis of a programming language with explicit support for representing, manipulating, and computing with variation in programs and data. We illustrate how such a language can simplify the implementation of variational programming tasks. We present the syntax and semantics of the core calculus, a sound type system, and a type inference algorithm that produces principal types.

Cite as

Sheng Chen, Martin Erwig, and Eric Walkingshaw. A Calculus for Variational Programming. In 30th European Conference on Object-Oriented Programming (ECOOP 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 56, pp. 6:1-6:28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)


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@InProceedings{chen_et_al:LIPIcs.ECOOP.2016.6,
  author =	{Chen, Sheng and Erwig, Martin and Walkingshaw, Eric},
  title =	{{A Calculus for Variational Programming}},
  booktitle =	{30th European Conference on Object-Oriented Programming (ECOOP 2016)},
  pages =	{6:1--6:28},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-014-9},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{56},
  editor =	{Krishnamurthi, Shriram and Lerner, Benjamin S.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2016.6},
  URN =		{urn:nbn:de:0030-drops-61005},
  doi =		{10.4230/LIPIcs.ECOOP.2016.6},
  annote =	{Keywords: Variational programming, variational types, variability-aware analyses}
}
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