The classic mean-variance optimization problem tries to minimize variance of a portfolio for a given expected return:
$$ \underset{w}{\arg \min} \quad w^T \Sigma w \quad \text{s.t} \quad \mu^Tw \geq \bar{\mu} $$
However the expected returns $\mu$ are can be computed in many different ways, but many use very subjective estimations. The problem is that the result of the optimization is very sensitive to these expected returns.
What asset allocation strategies exist where the expected returns are removed from the framework or where the sensitivity of the result (to changes in expected returns) is limited?
For example, the "min-variance" optimization removes the expected returns by return only the portfolio with minimum variance as follows:
$$ \underset{w}{\arg \min} \quad w^T \Sigma w$$