Structured Regression in Large Temporal Networks


In the first part of this talk, I will present a novel sampling-based structured regression method for prediction on top of temporal networks. The algorithm allows efficient learning of an ensemble model by automatically skipping the entire re-training or some phases of the training process in an evolving environment. In conducted experiments, the new method was about 140 times faster than alternative structured regression approaches while it was also more accurate as evident in modeling the H3N2 Virus Influenza network. The second part of the talk will describe an efficient algorithm to uncover the underlying dependency structure in high dimensional data. This is achieved by relying on Cholesky decomposition to learn a sparse Gaussian Markov Random Field. The new method is applied to discover the connectivity structure among gene expressions in septic patients. Results reported in this talk are published at:

  • Pavlovski, M., Zhou, F., Stojkovic, I., Kocarev, Lj., Obradovic, Z. "Adaptive Skip-Train Structured Regression for Temporal Networks," Proc. European Conf. Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Sept. 2017.
  • Stojkovic, I., Jelisavcic, V., Milutinovic, V., Obradovic, Z. "Fast Sparse Gaussian Markov Random Fields Learning Based on Cholesky Factorization," Proc. 26th International Joint Conference on Artificial Intelligence (IJCAI-17), Aug. 2017.
  • Han, C, Ghalwash, M., Obradovic, Z. "Continuous Conditional Dependent Network for Structured Regression," Proc. 31st AAAI Conference on Artificial Intelligence (AAAI-17), February 2017, 1962-1968.​

Brief Biography

Zoran Obradovic an Academician at the Academia Europaea (the Academy of Europe) and a Foreign Academician at the Serbian Academy of Sciences and Arts. He is an L.H. Carnell Professor of Data Analytics at Temple University, Professor in the Department of Computer and Information Sciences with a secondary appointment in the Department of Statistical Science, and is the Director of the Center for Data Analytics and Biomedical Informatics. His research interests include data science and complex networks in decision support systems. He has published more than 350 articles and is cited about 20,000 times (H-index 52). For more details see