  Regularization techniques are crucial to improving the generalization
performance and training efficiency of deep neural networks. Many deep learning
algorithms rely on weight decay, dropout, batch/layer normalization to converge
faster and generalize. Label Smoothing (LS) is another simple, versatile and
efficient regularization which can be applied to various supervised
classification tasks. Conventional LS, however, regardless of the training
instance assumes that each non-target class is equally likely. In this work, we
present a general framework for training with label regularization, which
includes conventional LS but can also model instance-specific variants. Based
on this formulation, we propose an efficient way of learning LAbel
regularization by devising a Bi-level Optimization (LABO) problem. We derive a
deterministic and interpretable solution of the inner loop as the optimal label
smoothing without the need to store the parameters or the output of a trained
model. Finally, we conduct extensive experiments and demonstrate our LABO
consistently yields improvement over conventional label regularization on
various fields, including seven machine translation and three image
classification tasks across various
