“Online Scalable Learning Adaptive to Unknown Dynamics and Graphs”
Thursday, March 7, 11:45 am
We live in an era of data deluge, where pervasive media collect massive amounts of data, often in a streaming fashion. Amajor portion of the data resides on networks representing a wide range of physical, biological, social, and financial interdependencies. Learning from these dynamic and large volumes of (network) datais hence expected to bring significant science and engineering advancesalong with consequent improvements in quality of life. However, with the blessings come big challenges. The sheer volume of data makes it impossible to run analytics in batch form. Large-scale datasets are noisy, incomplete, and prone to outliers. As many sources continuously generate data in real time, it is often impossible to store all of it. Thus, analytics must often be performed in real time, without a chance to revisit past entries. Furthermore, the networks on which the data reside can have very large size, and nodal attributes can be unavailable, e.g., due to privacy concerns. Meanwhile, new nodes can emerge over time, which can necessitate real-time evaluation of their nodal attributes. In response to these challenges, this talk will first introduce an online scalable function approximation scheme that is suitable for various machine learning tasks. The novel approach adaptively learns and tracks the sought nonlinear function ‘on the fly’ with quantifiable performance guarantees, even in adversarial environments with unknown dynamics. Building on this robust and scalable function approximation framework, a privacy-preserving graph-aware learning approach will be outlined next for learning over time-varying graphs with possibly growing sizes. Effectiveness of the novel algorithms will be showcased in several real-world datasets.
Yanning Shen received her B.Sc. and M.Sc. degrees in Electrical Engineering from the University of Electronic Science and Technology of China, in 2011 and 2014, respectively. Since September 2014, she has been working towards her Ph.D. degree with the Department of Electrical and Computer Engineering, University of Minnesota (UMN). Her research interests include data science, network science, machine learning, optimization and statistical signal processing. She received the UESTC distinguished B.Sc. thesis Award in 2011, and distinguished M.Sc. Thesis Award in 2014. She was a Best Student Paper Award finalist of the 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, and the 2017 Asilomar Conference on Signals, Systems, and Computers. She was selected as Rising Stars in EECS by Stanford University in 2017, and received the UMN Doctoral Dissertation Fellowship in 2018.