  We study the problem of multiset prediction. The goal of multiset prediction
is to train a predictor that maps an input to a multiset consisting of multiple
items. Unlike existing problems in supervised learning, such as classification,
ranking and sequence generation, there is no known order among items in a
target multiset, and each item in the multiset may appear more than once,
making this problem extremely challenging. In this paper, we propose a novel
multiset loss function by viewing this problem from the perspective of
sequential decision making. The proposed multiset loss function is empirically
evaluated on two families of datasets, one synthetic and the other real, with
varying levels of difficulty, against various baseline loss functions including
reinforcement learning, sequence, and aggregated distribution matching loss
functions. The experiments reveal the effectiveness of the proposed loss
function over the others.
