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Mathematics of Machine Learning
Recent advancements of machine learning methods lave led to breakthroughs in a wide range of applications. In spite of their empirical successes, the theoretical understanding of machine learning techniques are far from complete. New tools from mathematics and statistics have been showing their power in explaining the mystery and more will be emerging. The purpose of this reading seminar is threefold: First to discuss recent research works that lie in the interface of machine learning, mathematics, statistics and etc; second to expose new research questions and foster collaborations between different groups and even different departments; and third to create new research opportunities for junior researchers and graduate students. All faculty, VAPs, graduate students are welcome to join.
Date | Speaker | Title |
---|---|---|
Fri. 9/15
|
Wuchen Li University of South Carolina |
Information Gamma calculus: Convexity analysis for stochastic differential equations |
Fri. 9/22
|
Jack Xin University of California, Irvine |
Coarse Gradient Descent Method and Quantization of Deep Neural Networks |
Fri. 9/29
|
Siavash Jafarzadeh Lehigh University |
Reliable data-driven models for engineering design |
Fri. 10/13
|
Abiy Tasissa Tufts University |
TBA |
Fri. 10/20
|
Nan Chen University of Wisconsin-Madison |
TBA |
Fri. 10/27
|
Haizhao Yang University of Maryland, College Park |
Discretization-Invariant Operator Learning: Algorithms and Theory |
Fri. 11/3
|
Thomas Strohmer University of California, Davis |
TBA |
Fri. 11/17
|
Dejan Slepčev Carnegie Mellon University |
TBA |
Fri. 12/1
|
Maarten de Hoop Rice University |
TBA |
Department of Mathematics and Statistics