Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

Solving optimal control problems serves as the basic demand of industrial control tasks.
Existing methods like model predictive control often suffer from heavy online computational …

This article is a gentle discussion about the field of reinforcement learning in practice, about
opportunities and challenges, touching a broad range of topics, with perspectives and …

Recently, the field of generative models has seen a significant advancement with the
introduction of Diffusion Probabilistic Models (DPMs). The Denoising Diffusion Implicit Model …

We study embedding table placement for distributed recommender systems, which aims to
partition and place the tables on multiple hardware devices (eg, GPUs) to balance the …

Unlike perfect information games, where all elements are known to every player, imperfect
information games emulate the real-world complexities of decision-making under uncertain …

Despite the promising performance of deep reinforcement learning (DRL) agents in many
challenging scenarios, the black-box nature of these agents greatly limits their applications …

Cooperative multi-agent reinforcement learning (MARL) is making rapid progress for solving
tasks in a grid world and real-world scenarios, in which agents are given different attributes …

Embedding learning is an important technique in deep recommendation models to map
categorical features to dense vectors. However, the embedding tables often demand an …

Imbalanced learning is a fundamental challenge in data mining, where there is a
disproportionate ratio of training samples in each class. Over-sampling is an effective …