报告人： Kun Zhang (Carnegie Mellon University)
地点：Tecent Meeting (ID: 360 3614 4261)
Can we find the causal direction between two random variables without temporal precedence information? Can we figure out where latent causal variables should be and how they are related? In our daily life and science, people often attempt to answer such causal questions for the purpose of understanding, proper manipulation of systems, and robust prediction under interventions. Moreover, we are concerned with issues with artificial intelligence (AI) in complex environments. For instance, how can we do transfer learning in a principled way? How can machines deal with adversarial attacks? Interestingly, it has recently been shown that causal information can facilitate understanding and solving various AI problems. This talk focused on how to learn causal representations (with or without latent variables) from observational data and why and how the causal perspective allows adaptive prediction and a potentially higher level of artificial intelligence.
About the Speaker:
Kun Zhang is an associate professor of philosophy and an affiliate faculty in the machine learning department of Carnegie Mellon University. He has been actively developing methods for automated causal discovery from various kinds of data and investigating machine learning problems including transfer learning and representation learning from a causal perspective. He has been frequently serving as a senior area chair, area chair, or senior program committee member for conferences in machine learning or artificial intelligence, including NeurIPS, ICML, UAI, IJCAI, AISTATS, and ICLR, and has co-organized a number of conferences or workshops to foster interdisciplinary exploration of causality. He is a general & program chair of the first Conference on Causal Learning and Reasoning (CLeaR 2022) and a program chair of the 38th Conference on Uncertainty in Artificial Intelligene (UAI).
Your participation is warmly welcomed!