Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to
enable agents to learn and perform tasks autonomously with superhuman performance …

Mobile network operators (MNOs) allocate computing and caching resources for mobile
users by deploying a central control system. Existing studies mainly use programming and …

Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However,
alongside all its advancements, problems have also emerged, such as privacy violations …

Human-in-the-loop for reinforcement learning (RL) is usually employed to overcome the
challenge of sample inefficiency, in which the human expert provides advice for the agent …

Traditional cost-based optimizers are efficient and stable to generate optimal plans for
simple SQL queries, but they may not generate high-quality plans for complicated queries …

Embodied AI agents continue to become more capable every year with the advent of new
models, environments, and benchmarks, but are still far away from being performant and …

Temporal credit assignment is crucial for learning and skill development in natural and
artificial intelligence. While computational methods like the TD approach in reinforcement …

A major task of sports analytics is player evaluation. Previous methods commonly measured
the impact of players' actions on desirable outcomes (eg, goals or winning) without …

Action advising is a knowledge transfer technique for reinforcement learning based on the
teacher-student paradigm. An expert teacher provides advice to a student during training in …