Auditing the use of data in training machine-learning (ML) models is an increasingly
pressing challenge, as myriad ML practitioners routinely leverage the effort of content …

The text-to-image models based on diffusion processes, capable of transforming text
descriptions into detailed images, have widespread applications in art, design, and beyond …

Recent advancements in multi-agent reinforcement learning (MARL) have opened up vast
application prospects, such as swarm control of drones, collaborative manipulation by …

Recommender systems predict and suggest relevant options to users in various domains,
such as e-commerce, streaming services, and social media. Recently, deep reinforcement …

As the implementation of machine learning (ML) systems becomes more widespread,
especially with the introduction of larger ML models, we perceive a spring demand for …

Reinforcement learning (RL) trains an agent from experiences interacting with the
environment. In scenarios where online interactions are impractical, offline RL, which trains …