From noisy measurements, one can compute the worst-case upper bound of a basis-parameterized unknown function in closed form and iteratively refine it online until it reaches the true optimum.
Set membership estimation of nonlinear regressions,
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Set-valued regression and cautious suboptimization: From noisy data to optimality
From noisy measurements, one can compute the worst-case upper bound of a basis-parameterized unknown function in closed form and iteratively refine it online until it reaches the true optimum.