Junlin Wang

Margarita Geleta, Jiacen Xu, Manikanta Loya et al.

Although the prevention of AI vulnerabilities is critical to preserve the safety and privacy of users and businesses, educational tools for robust AI are still underdeveloped worldwide. We present the design, implementation, and assessment of Maestro. Maestro is an effective open-source game-based platform that contributes to the advancement of robust AI education. Maestro provides goal-based scenarios where college students are exposed to challenging life-inspired assignments in a competitive programming environment. We assessed Maestro's influence on students' engagement, motivation, and learning success in robust AI. This work also provides insights into the design features of online learning tools that promote active learning opportunities in the robust AI domain. We analyzed the reflection responses (measured with Likert scales) of 147 undergraduate students using Maestro in two quarterly college courses in AI. According to the results, students who felt the acquisition of new skills in robust AI tended to appreciate highly Maestro and scored highly on material consolidation, curiosity, and mastery in robust AI. Moreover, the leaderboard, our key gamification element in Maestro, has effectively contributed to students' engagement and learning. Results also indicate that Maestro can be effectively adapted to any course length and depth without losing its educational quality.

Jun-Lin Wang, Zi Xu

In this paper, we study second-order algorithms for solving nonconvex-strongly concave minimax problems, which have attracted much attention in recent years in many fields, especially in machine learning.We propose a gradient norm regularized trust-region (GRTR) algorithm to solve nonconvex-strongly concave minimax problems, where the objective function of the trust-region subproblem in each iteration uses a regularized version of the Hessian matrix, and the regularization coefficient and the radius of the ball constraint are proportional to the square root of the gradient norm. The iteration complexity of the proposed GRTR algorithm to obtain an $O(ε,\sqrtε)$-second-order stationary point is proved to be upper bounded by $\tilde{O}(\ell^{1.5}ρ^{0.5}μ^{-1.5}ε^{-1.5})$, where $μ$ is the strong concave coefficient, $\ell$ and $ρ$ are the Lipschitz constant of the gradient and Jacobian matrix respectively, which matches the best known iteration complexity of second-order methods for solving nonconvex-strongly concave minimax problems. We further propose a Levenberg-Marquardt algorithm with a gradient norm regularization coefficient and use the negative curvature direction to correct the iteration direction (LMNegCur), which does not need to solve the trust-region subproblem at each iteration. We also prove that the LMNegCur algorithm achieves an $O(ε,\sqrtε)$-second-order stationary point within $\tilde{O}(\ell^{1.5}ρ^{0.5}μ^{-1.5}ε^{-1.5})$ number of iterations.The inexact variants of both algorithms can still obtain $O(ε,\sqrtε)$-second-order stationary points with high probability, but only require $\tilde{O}(\ell^{2.25}ρ^{0.25}μ^{-1.75}ε^{-1.75})$ Hessian-vector products and $\tilde{O}(\ell^{2}ρ^{0.5}μ^{-2}ε^{-1.5})$ gradient ascent steps.