We introduce the learning to optimize (L2O) methodology to the minimax problems for the first time, and addresses its accompanying unique challenges. We present Twin L2O, the first dedicated minimax L2O framework consisting of two LSTMs for updating min and max variables, respectively. We then discuss a crucial concern of Twin-L2O, i.e., its inevitably limited generalizability to unseen optimizees, and present two complementary strategies.

We propose a unified model and embedding compression (UMEC) framework to hammer an efficient neural network-based recommendation system. Our framework jointly learns input feature selection and neural network compression together, and solve them as an end-to-end resource-constrained optimization problem using ADMM.

Optimizing the conventional Mean Square Error (MSE) loss results in a decoder matrix that spans the principal subspace of the sample covariance of the data, but, owing to an invariance that cancels out in the global map, it will fail to identify the exact eigenvectors. We show here that our proposed loss function eliminates this issue, so the decoder converges to the exact ordered unnormalized eigenvectors of the sample covariance matrix.