Keynote Speeches

Keynote Speech I

On Presuppositions of Machine Learning: A Best-fitting Theory

Machine learning have been applied with a set of prerequisite or hypotheses, the optimal setting of which is a `the chicken or the egg’ problem. Those hypotheses include in particular (i) the Large Capacity Hypothesis on hypothetical space, (ii) the Independence Hypothesis on loss function, (iii) the Completeness Hypothesis on training data, (iv) the Prior-Determine-Regularizer Hypothesis on regularization terms, and (v) the Euclidean Hypothesis on analysis framework. We analyze the role, effect and limitations of those hypotheses in this talk, and propose a systematic way, could named as a best-fitting theory, to break through each of the hypotheses.

More specifically, we propose the model driven deep learning approach to burst the Large Capacity Hypothesis, develop a noise modeling principle to breach the Independence Hypothesis, suggest the axiomatic curriculum/self-paced learning approach for the Completeness Hypothesis, the implicit regularization method for the Prior-Determine-Regularizer Hypothesis, and Banach space geometry for the Euclidean Hypothesis. In each case, we show the best-fitting strategy, substantiate the value and outcome of the breaking though. We show also that the continuing effort for bursting the hypotheses of ML is needed, which is then opening new hot directions of ML research.

Biosketch

Zongben Xu was born in 1955. He received his Ph.D. degrees in mathematics from Xi’an Jiaotong University, China, in 1987. His current research interests include applied mathematics and mathematical methods of big data and artificial intelligence. He established the L(1/2) regularization theory for sparse information processing. He also found and verified Xu-Roach Theorem in machine learning, and established the visual cognition based data modelling principle, which have been widely applied in scientific and engineering fields. He initiated several mathematical theories, including the non-logarithmic transform based CT model, and ultrafast MRI imaging, which provide principles and technologies for the development of a new generation of intelligent medical imaging equipment. He is owner of the Tan Kan Kee Science Award in Science Technology in 2018, the National Natural Science Award of China in 2007，and winner of CSIAM Su Buchin Applied Mathematics Prize in 2008. He delivered a 45-minute talk on the International Congress of Mathematicians 2010. He was elected as member of Chinese Academy of Science in 2011.

Zongben Xu was the vice-president of Xi’an Jiaotong University. He currently makes several important services for government and professional societies, including the director for Pazhou Lab (Guangzhou), director for the National Engineering Laboratory for Big Data Analytics, a member of National Big Data Expert Advisory Committee and the Strategic Advisory Committee member of National Open Innovation Platform for New Generation of Artificial Intelligence.