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PLDI 2020
Mon 15 - Fri 19 June 2020
Thu 18 Jun 2020 11:20 - 11:40 at PLDI Research Papers live stream - Machine Learning II Chair(s): Ke Wang

Numerical abstract domains are a key component of modern static analyzers. Despite recent advances, precise analysis with highly expressive domains remains too costly for many real-world programs. To address this challenge, we introduce a new data-driven method, called LAIT, that produces a faster and more scalable numerical analysis without significant loss of precision. Our approach is based on the key insight that sequences of abstract elements produced by the analyzer contain redundancy which can be exploited to increase performance without compromising precision significantly. Concretely, we present an iterative learning algorithm that learns a neural policy that identifies and removes redundant constraints at various points in the sequence. We believe that our method is generic and can be applied to various numerical domains.

We instantiate LAIT for the widely used Polyhedra and Octagon domains. Our evaluation of LAIT on a range of real-world applications with both domains shows that while the approach is designed to be generic, it is orders of magnitude faster on the most costly benchmarks than a state-of-the-art numerical library while maintaining close-to-original analysis precision. Further, LAIT outperforms hand-crafted heuristics and a domain-specific learning approach in terms of both precision and speed.

Thu 18 Jun
Times are displayed in time zone: Pacific Time (US & Canada) change

10:40 - 11:40: Machine Learning IIPLDI Research Papers at PLDI Research Papers live stream
Chair(s): Ke WangVisa Research

YouTube lightning session video

10:40 - 11:00
Proving Data-Poisoning Robustness in Decision Trees
PLDI Research Papers
Samuel DrewsUniversity of Wisconsin-Madison, USA, Aws AlbarghouthiUniversity of Wisconsin-Madison, USA, Loris D'AntoniUniversity of Wisconsin-Madison, USA
11:00 - 11:20
A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)
PLDI Research Papers
Muhammad UsmanUniversity of Texas at Austin, USA, Wenxi WangUniversity of Texas at Austin, USA, Marko VasicUniversity of Texas at Austin, USA, Kaiyuan WangGoogle, USA, Haris VikaloUniversity of Texas at Austin, USA, Sarfraz KhurshidUniversity of Texas at Austin, USA
11:20 - 11:40
Learning Fast and Precise Numerical Analysis
PLDI Research Papers
Jingxuan HeETH Zurich, Switzerland, Gagandeep SinghETH Zurich, Switzerland, Markus PüschelETH Zurich, Switzerland, Martin VechevETH Zurich, Switzerland