2023 IEEE CASS Seasonal School on Causal Learning and Reasoning (CLR)

Deep learning has achieved great success in a wide variety of fields. With an end-to-end training, a deep model can effectively learn the correlation of samples and labels. However, such correlation may contain spurious bias, and it is volatile under distribution shift. There is a growing consensus that causality is the key to solving these issues. Causal learning removes the spurious correlation and discovers the causal knowledge in data, which can improve the generalizability and robustness of models. Moreover, causal reasoning based on the learned causal relationship enables the building of a more interpretable and reliable learning system.

We plan to organize the IEEE CASS Seasonal School on Causal Learning and Reasoning (CLR) with multimodal data in order to promote the frontier of causality and machine learning research and to provide an ambiance for fruitful exchange of ideas and discussion of advances, challenges and trends of this area, bringing together experts from both academia and industry. In this seasonal school, we will invite several top-tier researchers to present their works and arrange two panels to discuss the new trends of causal learning and reasoning.

This seasonal school will be held in National SuperComputer Center (Guangzhou), on March 19-21, 2023. The target audiences are the community of Visual & Language Signal Processing and Applications, composed by graduate and undergraduate students, professors, as well as industry or research professionals.

This seasonal school severs as a platform allowing scholars, technological researchers, and the industry to communicate with each other on the existing advances, unresolved challenges, and promising trends of causal learning and reasoning with multimodal data, to further accelerate the growth in this field. Accordingly, the invited well-known professional researchers are from not only academy but also industry. On one hand, this will be meaningful for students and researchers to know more about the advances and unresolved problems from a practical view. On the other hand, those working in the industry will be sparked by cutting-edge research works and theoretical foundations.