CAREER: Learning in Adversarial and Nonstationary Environments

大多数机器学习算法都依赖于这样的假设，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非最佳甚至可靠。最近的工作表明，对手可以显着妥协的预处理技术和分类的结果。不幸的是，一个统一的框架，学习的对手从流数据的存在还没有得到解决，尽管越来越多的应用程序，需要这样的技术。这个职业生涯将研究了解何时以及为什么特征选择与对手失败。这项研究不仅将集中在理解为什么特征选择失败，而且还转移的黑盒和白色盒攻击的特征选择。该项目还提出开发新的方法来攻击信息理论算法和弹性信息理论特征选择方法。这个职业生涯还解决了在存在对手的非平稳环境中学习的问题。将为每项任务执行一套综合的和真实世界的基准测试。该研究重点关注这一未满足的需求，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：特别是非平稳环境中的特征选择和学习算法。教育计划包括指导和培训数据科学家的未来劳动力，他们目前需求量很大，通过在大学的协作学习环境中通过多层次教育引入机器学习。CAREER项目还包括整合研究，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进行STEM和机器学习。这个职业将吸引图森的K-12学生，通过实践教学技术促进STEM教育和机器学习。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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.