Curriculum Learning: Theories, Approaches, Applications, Tools, and Future Directions in the Era of Large Language Models

This tutorial focuses on curriculum learning (CL), an important topic in machine learning, which gains an increasing amount of attention in the research community. CL is a learning paradigm that enables machines to learn from easy data to hard data, imitating the meaningful procedure of human learning with curricula. As an easy-to-use plug-in, CL has demonstrated its power in improving the generalization capacity and convergence rate of various models in a wide range of scenarios such as computer vision, natural language processing, data mining, reinforcement learning, etc. Therefore, it is essential introducing CL to more scholars and researchers in the machine learning community. However, there have been no tutorials on CL so far, motivating the organization of our tutorial on CL at WWW 2024. To give a comprehensive tutorial on CL, we plan to organize it from the following aspects: (1) theories, (2) approaches, (3) applications, (4) tools and (5) future directions. First, we introduce the motivations, theories and insights behind CL. Second, we advocate novel, high-quality approaches, as well as innovative solutions to the challenging problems in CL. Then we present the applications of CL in various scenarios, followed by some relevant tools. In the end, we discuss open questions and the future direction in the era of large language models. We believe this topic is at the core of the scope of WWW and is attractive to the audience interested in machine learning from both academia and industry.