Machine Learning and Solvers: The Next Frontier

机器学习和求解器:下一个前沿

基本信息

  • 批准号:
    RGPIN-2020-05106
  • 负责人:
  • 金额:
    $ 2.99万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2020
  • 资助国家:
    加拿大
  • 起止时间:
    2020-01-01 至 2021-12-31
  • 项目状态:
    已结题

项目摘要

Software security and reliability remain one of the most important and challenging technological problems we face today. As a consequence, there is an ever-present demand for scalable and effective testing, analysis, and verification (TAV) methods. Further, as machine learning (ML) systems continue to revolutionize many areas of engineering and industry, their reliability and security have become a grave concern. Hackers have found novel ways of launching adversarial attacks against ML systems. Fortunately, successful TAV methods developed for general software can also be adapted to address the reliability and security problems of ML systems. A critical component in many scalable and effective TAV methods is a logic (aka, SAT or SMT) solver, systems that automatically solve mathematical constraints obtained from analysis of programs. Interestingly, today's leading logic solvers inherently rely on ML for improved performance, a line of research that I pioneered over the last 6 years. Conversely, one powerful way of improving the reliability and security of ML systems is via logic-guided ML systems, another line of research I have recently been developing. This Discovery Grant will fund a bleeding-edge long-term research program with the following broad directions: ML for Logic Solvers: First, we propose to develop novel ML-based methods aimed at making solver algorithms even more efficient. A logic solver can be viewed as a collection of interacting and dynamic heuristics that aim to optimally initialize, select, and sequence proof rules for a given input formula. These optimization problems are best solved using online ML methods. I propose to develop novel deep neural networks (DNNs) and deep reinforcement learning (DRL) based online and dynamic methods to initialize, sequence, and select powerful proof rules inside solvers. We will develop these methods for SMT and extended resolution solvers. Logic Solvers for ML: Second, we propose to develop a set of logic-based ML algorithms, called Logic Guided Machine Learning (LGML), that use solvers to verify, correct, and adversarially train ML models. The key insight is to combine solvers and ML models in a corrective feedback loop in order verify the ML models, and retrain them as necessary via 'optimal' counterexamples. The proposed research will have deep fundamental scientific, technical, and commercial impact. These results will not only enable improved ML-based solver design (co-developed with cryptanalysis and TAV methods for software and ML systems), but also logic-guided ML systems that are more reliable and robust against adversarial attacks. Further, the program aims at a deeper foundational understanding of why solver work at all, and thus paving the way for a more scientific approach to solver design. Most importantly, the program aims to train at least 6 HQP in logic solvers, ML, and combinations thereof, aimed at secure and reliable software and ML systems.
软件安全性和可靠性仍然是我们今天面临的最重要和最具挑战性的技术问题之一。因此,对可伸缩且有效的测试、分析和验证(TAV)方法的需求一直存在。此外,随着机器学习(ML)系统不断改变工程和工业的许多领域,它们的可靠性和安全性已经成为一个严重的问题。黑客已经找到了针对机器学习系统发起对抗性攻击的新方法。幸运的是,为一般软件开发的成功的TAV方法也可以用于解决机器学习系统的可靠性和安全性问题。

项目成果

期刊论文数量(0)
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会议论文数量(0)
专利数量(0)

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Ganesh, Vijay其他文献

EXE: Automatically Generating Inputs of Death
Complex Golay pairs up to length 28: A search via computer algebra and programmatic SAT
  • DOI:
    10.1016/j.jsc.2019.10.013
  • 发表时间:
    2021-01-01
  • 期刊:
  • 影响因子:
    0.7
  • 作者:
    Bright, Curtis;Kotsireas, Ilias;Ganesh, Vijay
  • 通讯作者:
    Ganesh, Vijay
Transcriptome and Genome Analysis Uncovers a DMD Structural Variant: A Case Report.
  • DOI:
    10.1212/nxg.0000000000200064
  • 发表时间:
    2023-04
  • 期刊:
  • 影响因子:
    3.1
  • 作者:
    Folland, Chiara;Ganesh, Vijay;Weisburd, Ben;McLean, Catriona;Kornberg, Andrew J.;O'Donnell-Luria, Anne;Rehm, Heidi L.;Stevanovski, Igor;Chintalaphani, Sanjog R.;Kennedy, Paul;Deveson, Ira W.;Ravenscroft, Gianina
  • 通讯作者:
    Ravenscroft, Gianina
A nonexistence certificate for projective planes of order ten with weight 15 codewords
Applying computer algebra systems with SAT solvers to the Williamson conjecture
  • DOI:
    10.1016/j.jsc.2019.07.024
  • 发表时间:
    2020-09-01
  • 期刊:
  • 影响因子:
    0.7
  • 作者:
    Bright, Curtis;Kotsireas, Ilias;Ganesh, Vijay
  • 通讯作者:
    Ganesh, Vijay

Ganesh, Vijay的其他文献

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{{ truncateString('Ganesh, Vijay', 18)}}的其他基金

Machine Learning and Solvers: The Next Frontier
机器学习和求解器:下一个前沿
  • 批准号:
    RGPIN-2020-05106
  • 财政年份:
    2022
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Machine Learning and Solvers: The Next Frontier
机器学习和求解器:下一个前沿
  • 批准号:
    RGPIN-2020-05106
  • 财政年份:
    2021
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Next-generation Constraint Solvers for Software Engineering and Security
用于软件工程和安全的下一代约束求解器
  • 批准号:
    435967-2013
  • 财政年份:
    2019
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Next-generation Constraint Solvers for Software Engineering and Security
用于软件工程和安全的下一代约束求解器
  • 批准号:
    435967-2013
  • 财政年份:
    2018
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Next-generation Constraint Solvers for Software Engineering and Security
用于软件工程和安全的下一代约束求解器
  • 批准号:
    435967-2013
  • 财政年份:
    2017
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Next-generation Constraint Solvers for Software Engineering and Security
用于软件工程和安全的下一代约束求解器
  • 批准号:
    435967-2013
  • 财政年份:
    2015
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Next-generation Constraint Solvers for Software Engineering and Security
用于软件工程和安全的下一代约束求解器
  • 批准号:
    435967-2013
  • 财政年份:
    2014
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Next-generation Constraint Solvers for Software Engineering and Security
用于软件工程和安全的下一代约束求解器
  • 批准号:
    435967-2013
  • 财政年份:
    2013
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual

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