However, the nonlinearity of risk measures makes it challenging to achieve convergence and optimality.

Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects.

We use decision trees to convey this reasoning information, as they can be easily represented in natural language, effectively providing knowledge from prior experiments ( i.e., the impact of the generated features on performance) to

Therefore, it has a high decoding speed but an unsatisfactory acceptance rate.

However, an emerging issue in allocation problems is data privacy.