In 2019, I serve as a reviewer/PC member of ICLR, AISTATS, ICML, IJCAI, GSI, ICCV, UAI, NeurIPS
Check our latest work on Lightlike Neuromanifolds, Occam’s Razor and Deep Learning which explains generalization of deep learning using geometric tools from general relativity.
I am attending UAI (July 22-25 2019) to present our recent work on Fisher-Bures Adversary Graph Convolutional Networks.
I am a machine learning scientist. My interest lies in the spectrum of unsupervised learning, Bayesian statistical learning, manifold learning, and deep learning. I formulate statistical learning problems from the perspective of information geometry (think of general relativity as the curvature of spacetime; information geometry is about the curvature of information), which tackles the essence of information using elegant mathematical tools. A goal of my research is to build a geometric theory of intelligence. I like simple and profound methodologies that are (anti-)intuitive, and therefore my works are more conceptual than practical. Recently I have been focusing on machine learning on graph-structured data.