Small Dataset, Big Gains: Enhancing Reinforcement Learning by Offline Pre-Training with Model-Based Augmentation

With the increasing appetite for data in data-driven methods, the issues of biased and scarce data have become a major bottleneck in developing generalizable and scalable artificial intelligence solutions, as well as effective deployment of these solutions in real-world scenarios. To tackle these challenges, researchers from both academia and industry must collaborate and make progress in fundamental research and applied technologies. The organizing committee and keynote speakers of AIBSD 2024 consist of experts from both academia and industry with rich experiences in designing and developing robust artificial intelligence algorithms and transferring them to real-world solutions. AIBSD 2024 provides a focused venue to discuss and disseminate research related to bias and scarcity topics in artificial intelligence.