However,the criteria by which the prediction model is trained are often inconsistent with the goal of the downstream optimization problem. Recently, decision-focused prediction approaches, such as SPO+ and direct optimization, have been proposed to fill this gap. However, they cannot directly handle the soft constraints with the max operator required in many real-world objectives.

The domain-specific representation is optimized through the meta-learning framework to adapt from source domains, targeting a robust generalization on unseen domains. We empirically show that mDSDI provides competitive results with state-of-the-art techniques in DG.

Sampling from an unnormalized target distribution is an essential problem with manyapplications inprobabilistic inference.

Moreover, they have also been the most quantitatively accurate predictive models of neuronal responses indifferentsensory areasintheprimate brain[57,31,5].