CAREER: Learning in Adversarial and Nonstationary Environments

大多数机器学习算法依赖于这样的假设，即数据是从固定的概率分布中采样的。这一假设在实践中经常被违反，这导致分类和回归策略远远不是最优的，甚至是可靠的。最近的研究表明，对手可以显著损害预处理技术和分类的结果。不幸的是，尽管越来越多的应用程序需要这种技术，但还没有解决在对手在场的情况下从流数据中学习的统一框架。这一职业将学习理解在对手的情况下，特征选择失败的时间和原因。这项研究不仅将重点放在了解特征选择失败的原因上，还将关注黑白盒攻击对特征选择的可转移性。该项目还建议开发新的方法来攻击信息论算法和弹性信息论特征选择方法。这一职业还解决了在有对手在场的不稳定环境中学习的问题。将为每项任务执行一套全面的综合和现实世界基准。该研究着眼于这一未得到满足的需求，并解决了来自机器学习不同子领域的各种对抗性学习问题：具体地说，非平稳环境下的特征选择和学习算法。该教育计划包括指导和培训数据科学家的未来劳动力，通过在大学的协作学习环境中通过多个层次的教育引入机器学习，目前数据科学家的需求很高。该职业项目还包括综合研究、修订研究和学习，并以社区为基础整合教育研究，以吸引更多各级学生参加STEM和机器学习。这一职业将吸引图森的K-12学生来促进STEM教育，并通过实践教学技术促进机器学习。还将根据职业研究成果和机器学习的最新趋势，向数据科学界进行公开演讲。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The majority of machine learning algorithms rely on the assumption that data are sampled from a fixed probability distribution. This assumption is often violated in practice, which results in classification and regression strategies that are far from optimal or even reliable. Recent work has shown that an adversary can significantly compromise the outcome of preprocessing techniques and classification. Unfortunately, a unified framework for learning in the presence of an adversary from streaming data has not been addressed despite the growing number of applications that need such techniques. This CAREER will study to understand when and why feature selection fails with an adversary. Not only will this research focus on understanding why feature selection fails, but also the transferability of black and white box attacks on feature selection. This project also proposes to develop novel methods to attack information-theoretic algorithms and approaches for resilient information-theoretic feature selection. This CAREER also addresses the problem of learning in a nonstationary environment with the presence of an adversary. A comprehensive set of synthetic and real-world benchmarks will be performed for each of the tasks. The research focuses on this unmet need and tackles a variety of adversarial learning problems drawn from different subfields of machine learning: specifically, algorithms for feature selection and learning in nonstationary environments.A successful implementation of the proposed research plan will have broader impacts on machine learning and application-driven domains. The education plan includes mentoring and training the future workforce for data scientists, who are currently in high demand, by introducing machine learning through multiple levels of education in a collaborative learning environment at the university. The CAREER project also includes integrated then integration research, revise research and learning with a community-based integration of research in education to draw more students at all levels for STEM and machine learning. This CAREER will engage K-12 students in Tucson to promote STEM education and also machine learning through hands-on teaching techniques. There will also be public talks to the data science community based on the CAREER research outcomes and the most recent trends in machine learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.