Before revision:  To demonstrate the power and the flexibility of our framework , we apply the framework on three very different learning problems : stochastic gradient descent for strongly convex functions, streaming principal component analysis and linear bandit with stochastic gradient descent updates.
After revision:  To demonstrate the power and the flexibility of our framework , we apply the framework on three very different learning problems with both the last iterate and the strong uniform high probability convergence guarantee. The problems are stochastic gradient descent for strongly convex functions, streaming principal component analysis and linear bandit with stochastic gradient descent updates.