This paper surveys the recent attempts, both from the machine learning and operations
research communities, at leveraging machine learning to solve combinatorial optimization …

We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …

We demonstrate that scaling up language models greatly improves task-agnostic, few-shot
performance, sometimes even becoming competitive with prior state-of-the-art fine-tuning …

Deep neural networks can achieve great successes when presented with large data sets
and sufficient computational resources. However, their ability to learn new concepts quickly …

Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …

Training models that generalize to new domains at test time is a problem of fundamental
importance in machine learning. In this work, we encode this notion of domain …

Developing neural network image classification models often requires significant
architecture engineering. In this paper, we study a method to learn the model architectures …

Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for
quickly improving performance on a novel task. Bayesian hierarchical modeling provides a …

Transfer learning is an emerging topic that may drive the success of machine learning in
research and industry. The lack of data on specific tasks is one of the main reasons to use it …

Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …