Fri 17 Jun 2022 02:30 - 02:50 at Toucan - Numbers
In CS theory courses, proofs by reduction are a notorious source of pain for students and instructors alike. Invariably, students use pen and paper to write down reductions that “work” in many but not all cases. When instructors observe that a student’s reduction deviates from the expected one, they have to manually compute a counterexample that exposes the mistake. In other words, reductions are subtle yet, most of the time, unimplemented programs. And for a good reason: there exists no language tailored to reductions.
We introduce Karp, a language for programming and testing Karp reductions. Karp combines an array of programming languages techniques: language-oriented programming and macros, solver-aided languages, property testing, higher-order contracts and gradual typing. To validate the correctness of Karp, we prove that its core is well-defined. To validate its pragmatics, we demonstrate that it is expressive and performant enough to handle a diverse set of reduction exercises from a popular algorithms textbook. Finally, we report from a preliminary user study with Karp.
Thu 16 JunDisplayed time zone: Pacific Time (US & Canada) change
13:30 - 14:50 | |||
13:30 20mTalk | Choosing Mathematical Function Implementations for Speed and Accuracy PLDI DOI | ||
13:50 20mTalk | Guaranteed bounds for posterior inference in universal probabilistic programming PLDI Raven Beutner CISPA Helmholtz Center for Information Security, Germany, C.-H. Luke Ong University of Oxford, Fabian Zaiser University of Oxford DOI Pre-print | ||
14:10 20mTalk | Progressive Polynomial Approximations for Fast Correctly Rounded Math Libraries PLDI Mridul Aanjaneya Rutgers University, Jay P. Lim Yale University, Santosh Nagarakatte Rutgers University Link to publication DOI Pre-print | ||
14:30 20mTalk | Karp: A Language for NP Reductions PLDI Chenhao Zhang Northwestern University, Jason D. Hartline Northwestern University, Christos Dimoulas PLT @ Northwestern University DOI |
Fri 17 JunDisplayed time zone: Pacific Time (US & Canada) change
01:30 - 02:50 | |||
01:30 20mTalk | Choosing Mathematical Function Implementations for Speed and Accuracy PLDI DOI | ||
01:50 20mTalk | Guaranteed bounds for posterior inference in universal probabilistic programming PLDI Raven Beutner CISPA Helmholtz Center for Information Security, Germany, C.-H. Luke Ong University of Oxford, Fabian Zaiser University of Oxford DOI Pre-print | ||
02:10 20mTalk | Progressive Polynomial Approximations for Fast Correctly Rounded Math Libraries PLDI Mridul Aanjaneya Rutgers University, Jay P. Lim Yale University, Santosh Nagarakatte Rutgers University Link to publication DOI Pre-print | ||
02:30 20mTalk | Karp: A Language for NP Reductions PLDI Chenhao Zhang Northwestern University, Jason D. Hartline Northwestern University, Christos Dimoulas PLT @ Northwestern University DOI |