I am interested in building scalable, reliable and efficient probabilistic models for machine learning and data science. Currently, I focus on developing fast and robust inference methods with theoretical guarantees and their applications with modern model architectures, such as deep neural networks, on real-world big data.
Ruqi Zhang, Zhiwu Lu International Joint Conference on Artificial Intelligence (IJCAI), 2016 [Paper]
Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang Symposium on Advances in Approximate Bayesian Inference (AABI), 2019 [Paper]
Spotlight presentation at NeurIPS, December 2019 [Slides]