Mohammad Pedramfar

Logarithmic sampling regret is achievable in online learning by leveraging squared confidence widths, a fundamental departure from standard regret minimization.

Unlock $O(T^{1/2})$ regret for non-monotone DR-submodular maximization with just one gradient query per round by cleverly linearizing the problem.