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Learning with Temporal Point Processes
Manuel Gomez-Rodriguez · Isabel Valera

Tue Jul 10 12:15 AM -- 02:30 AM (PDT) @ K1 + K2
Event URL: http://learning.mpi-sws.org/tpp-icml18/ »

In recent years, there has been an increasing number of machine learning models, inference methods and control algorithms using temporal point processes. They have been particularly popular for understanding, predicting, and enhancing the functioning of social and information systems, where they have achieved unprecedented performance. This tutorial aims to introduce temporal point processes to the machine learning community at large. In the first part of the tutorial, we will first provide an introduction to the basic theory of temporal point processes, then revisit several types of points processes, and finally introduce advanced concepts such as marks and dynamical systems with jumps. In the second and third parts of the tutorial, we will explain how temporal point processes have been used in developing a variety of recent machine learning models and control algorithms, respectively. Therein, we will revisit recent advances related to, e.g., deep learning, Bayesian nonparametrics, causality, stochastic optimal control and reinforcement learning. In each of the above parts, we will highlight open problems and future research to facilitate further research in temporal point processes within the machine learning community.


Author Information

Manuel Gomez-Rodriguez (MPI-SWS)
Manuel Gomez-Rodriguez

Manuel Gomez Rodriguez is a faculty at Max Planck Institute for Software Systems. Manuel develops human-centric machine learning models and algorithms for the analysis, modeling and control of social, information and networked systems. He has received several recognitions for his research, including an outstanding paper award at NeurIPS’13 and a best research paper honorable mention at KDD’10 and WWW’17. He has served as track chair for FAT* 2020 and as area chair for every major conference in machine learning, data mining and the Web. Manuel has co-authored over 50 publications in top-tier conferences (NeurIPS, ICML, WWW, KDD, WSDM, AAAI) and journals (PNAS, Nature Communications, JMLR, PLOS Computational Biology). Manuel holds a BS in Electrical Engineering from Carlos III University, a MS and PhD in Electrical Engineering from Stanford University, and has received postdoctoral training at the Max Planck Institute for Intelligent Systems.

Isabel Valera (Max Planck Institute for Intelligent Systems)

Isabel Valera is a Minerva research group leader at the Max Planck Institute for Intelligent Systems. Isabel develops flexible and efficient probabilistic models and inference algorithms to fit and analyze real-world data. She is particularly interested in problems related to the unstructured and complex nature of real-world data, which are often time-dependent, heterogeneous, noisy, and might contain errors and missing values. Isabel obtained her PhD in 2014 and her MSc degree in 2012, both from the University Carlos III in Madrid, Spain. She has been a German Humboldt Post-Doctoral Fellowship Holder, and recently she has been granted with a Minerva fast track research group from the Max Planck Society. You can find more about her at https://ivaleram.github.io/.

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