CAREER: Learning in Adversarial and Nonstationary Environments

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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.