Abstract We explore why many recently proposed robust estimation problems are efficiently solvable, even though the underlying optimization non-convex. study loss landscape of these problems, and identify existence ’generalized quasi-gradients’. Whenever quasi-gradients exist, a large family no-regret algorithms guaranteed to approximate global minimum; this includes commonly used filtering alg...