Decision making in these systems has a long history, yet, if the state is not fully observed acting optimally in such systems is notoriously hard.

Pn i=1 (ti t) andthe (x, tN(t)) (x, t), whichsets (x, tN(t)), see Otheroptimal 25, 36].

In this paper, we first propose a flexible framework for safe screening based on the Fenchel-Rockafellar duality and then deriveastrong safe screening rule for norm-regularized least squares using the proposed framework.

They are able to learn anti-correlated instances, i.e., defaulting to "positive" labels until seeing a negative counter-example, which should not be possible for a correct MIL model.

Moreover,tofurther accelerate overgreedy descent methods, wepresent a new accelerated random search (ARS) algorithm that incorporates prior information, together with aconvergence analysis.