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Learning with Temporal Point Processes
Manuel Rodriguez · Le Song · Isabel Valera · Yan Liu · Abir De · Hongyuan Zha

Sat Dec 14 08:00 AM -- 06:00 PM (PST) @ West 306
Event URL: https://sites.google.com/view/tpp-neurips-2019 »

In recent years, there has been an increasing number of machine learning models and algorithms based on the theory of temporal point processes, which is a mathematical framework to model asynchronous event data. These models and algorithm have found a wide range of human-centered applications, from social and information networks and recommender systems to crime prediction and health. Moreover, this emerging line of research has already established connections to deep learning, deep generative models, Bayesian nonparametrics, causal inference, stochastic optimal control and reinforcement learning. However, despite these recent advances, learning with temporal point processes is still a relatively niche topic within the machine learning community---there are only a few research groups across the world with the necessary expertise to make progress. In this workshop, we aim to popularize temporal point processes within the machine learning community at large. In our view, this is the right time to organize such a workshop because, as algorithmic decisions becomes more consequential to individuals and society, temporal point processes will play a major role on the development of human-centered machine learning models and algorithms accounting for the feedback loop between algorithmic and human decisions, which are inherently asynchronous events. Moreover, it will be a natural follow up of a very successful and well-attended ICML 2018 tutorial on learning with temporal point processes, which two of us recently taught.

Sat 8:30 a.m. - 8:35 a.m.
Welcome Address and Introduction
Sat 8:35 a.m. - 9:15 a.m.
Invited Talk by Negar Kiyavash (Invited Talk)
Negar Kiyavash
Sat 9:15 a.m. - 9:30 a.m.
Fused Gromov-Wasserstein Alignment for Hawkes Processes (Presentation 1)
Sat 9:30 a.m. - 9:45 a.m.
Insider Threat Detection via Hierarchical Neural Temporal Point Processes (Presentation 2)
Xintao Wu
Sat 9:45 a.m. - 10:30 a.m.
Coffee Break (Break)
Sat 10:30 a.m. - 11:10 a.m.
Invited Talk By Niloy Ganguly (Invited Talk)
Niloy ganguly
Sat 11:10 a.m. - 11:25 a.m.
Intermittent Demand Forecasting with Deep RenewalProcesses (Presentation 3)
Caner Turkmen
Sat 11:25 a.m. - 11:40 a.m.
Temporal Logic Point Processes (Presentation 4)
Sat 11:40 a.m. - 11:55 a.m.
The Graph Hawkes Network for Reasoning on Temporal Knowledge Graphs (Presentation 5)
Sat 11:55 a.m. - 12:10 p.m.
Multivariate coupling estimation between continuous signals and point processes (Presentation 6)
Michel Besserve
Sat 12:10 p.m. - 1:50 p.m.
Lunch Break (Break)
Sat 1:50 p.m. - 2:30 p.m.
Invited Talk by Walter Dempsey (Invited Talk)
Walter Dempsey
Sat 2:30 p.m. - 2:45 p.m.
Better Approximate Inference for Partial Likelihood Models with a Latent Structure (Presentation 7)
Amrith Setlur
Sat 2:45 p.m. - 2:45 p.m.
Deep Point Process Destructors (Presentation 8)
Sat 3:00 p.m. - 3:15 p.m.
A sleep-wake detection algorithm for memory-constrained wearable devices: Change Point Decoder (Presentation 9)
Ayse Selin Cakmak
Sat 3:15 p.m. - 3:30 p.m.
Topics are not Marks: Modeling Text-based Cascades using Multi-network Hawkes Process (Presentation 10)
Jayesh Choudhari
Sat 3:30 p.m. - 4:45 p.m.
Poster Setup + Coffee Break (Break)
Sat 4:15 p.m. - 5:00 p.m.
Temporal point process models vs. discrete time models (Panel discussion)
Sat 5:00 p.m. - 6:00 p.m.
Poster Session
Ayse Selin Cakmak, Yunkai Zhang, Srijith Prabhakarannair Kusumam, Mohamed Osama Ahmed, Xintao Wu, Jayesh Choudhari, David I Inouye, Tom Taylor, Michel Besserve, Caner Turkmen, Kazi Islam, Antonio Artés, Amrith Setlur, Zhanghua Fu, Zhen Han, Abir De, Nan Du, Pablo Sanchez Martin

Author Information

Manuel Rodriguez (MPI SWS)
Le Song (Georgia Institute of Technology)
Isabel Valera (Max Planck Institute for Intelligent Systems)
Yan Liu (University of Southern California)
Abir De (MPI-SWS)
Hongyuan Zha (Georgia Tech)

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