Collaborative Research: RI: Small: Advancing Theory and Practice of Trustworthy Machine Learning via Bi-Level Optimization

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Deep learning (DL) has achieved remarkable success owing to its superior prediction ability, with a wide range of applications in computer vision and natural language processing. Yet, one of its critical shortcomings is the lack of trustworthiness. That is, they are often overcooked during training such that (1) the learned model is highly vulnerable to small input perturbations at the testing time (namely, lack of robustness); And (2) biased artifacts embedded in the training data can be memorized and then passed on to the decision making process (namely, lack of fairness). To address these issues, this project attempts to develop a new family of trustworthy learning algorithms with algorithmic generality, theoretical soundness, and scalability to large-scale datasets and models. The outcome of this project could create a new optimization foundation of trustworthy DL that can not only unit robustness and fairness into one coherent learning paradigm but also expand the applicability of DL to a series of high-stakes applications such as autonomous driving and cybersecurity. Interdisciplinary training in computer science, applied mathematics, and engineering will be provided to all-level students, especially for students from underrepresented groups. The main technical aim of this project is to advance the theoretical understanding and practical implementations of trustworthy DL through the lens of bi-level optimization (BLO), namely, hierarchical learning involving two nested optimization tasks. The research plan consists of three thrusts. The first thrust develops a new BLO-oriented robust learning framework including defenses against adversarial instances and distribution shifts. The developed technique is also applied to building a full-stack (from train time to test time) robustness evaluation pipeline. The second thrust expands the first one and develops BLO algorithms to co-improve robustness and fairness in two practical scenarios, learning without sensitive attribute annotation, and learning with scarce training data and model information. The third thrust focuses on developing scalable and theoretically-grounded computational methods for BLO so as to achieve a high-accuracy, high-resilience, and high-throughput trustworthy learning paradigm. The project will result in the dissemination of shared toolbox and benchmarks to the broader optimization and machine learning communities.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.