Timezone: »
A parametric point process model is developed, with modeling based on the assumption that sequential observations often share latent phenomena, while also possessing idiosyncratic effects. An alternating optimization method is proposed to learn a registered'' point process that accounts for shared structure, as well as
warping'' functions that characterize idiosyncratic aspects of each observed sequence. Under reasonable constraints, in each iteration we update the sample-specific warping functions by solving a set of constrained nonlinear programming problems in parallel, and update the model by maximum likelihood estimation. The justifiability, complexity and robustness of the proposed method are investigated in detail, and the influence of sequence stitching on the learning results is examined empirically. Experiments on both synthetic and real-world data demonstrate that the method yields explainable point process models, achieving encouraging results compared to state-of-the-art methods.
Author Information
Hongteng Xu (InfiniaML, Inc.)
Lawrence Carin (Duke)
Hongyuan Zha (Georgia Institute of Technology)
Related Events (a corresponding poster, oral, or spotlight)
-
2018 Oral: Learning Registered Point Processes from Idiosyncratic Observations »
Fri Jul 13th 07:30 -- 07:50 AM Room A3
More from the Same Authors
-
2020 Poster: Learning Autoencoders with Relational Regularization »
Hongteng Xu · Dixin Luo · Ricardo Henao · Svati Shah · Lawrence Carin -
2020 Poster: Transformer Hawkes Process »
Simiao Zuo · Haoming Jiang · Zichong Li · Tuo Zhao · Hongyuan Zha -
2020 Poster: Graph Optimal Transport for Cross-Domain Alignment »
Liqun Chen · Zhe Gan · Yu Cheng · Linjie Li · Lawrence Carin · Jingjing Liu -
2020 Poster: On Leveraging Pretrained GANs for Generation with Limited Data »
Miaoyun Zhao · Yulai Cong · Lawrence Carin -
2020 Poster: GraphOpt: Learning Optimization Models of Graph Formation »
Rakshit Trivedi · Jiachen Yang · Hongyuan Zha -
2020 Poster: CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information »
Pengyu Cheng · Weituo Hao · Shuyang Dai · Jiachang Liu · Zhe Gan · Lawrence Carin -
2019 Poster: Gromov-Wasserstein Learning for Graph Matching and Node Embedding »
Hongteng Xu · Dixin Luo · Hongyuan Zha · Lawrence Carin -
2019 Poster: On Scalable and Efficient Computation of Large Scale Optimal Transport »
Yujia Xie · Minshuo Chen · Haoming Jiang · Tuo Zhao · Hongyuan Zha -
2019 Oral: Gromov-Wasserstein Learning for Graph Matching and Node Embedding »
Hongteng Xu · Dixin Luo · Hongyuan Zha · Lawrence Carin -
2019 Oral: On Scalable and Efficient Computation of Large Scale Optimal Transport »
Yujia Xie · Minshuo Chen · Haoming Jiang · Tuo Zhao · Hongyuan Zha -
2019 Poster: Stochastic Blockmodels meet Graph Neural Networks »
Nikhil Mehta · Lawrence Carin · Piyush Rai -
2019 Poster: Variational Annealing of GANs: A Langevin Perspective »
Chenyang Tao · Shuyang Dai · Liqun Chen · Ke Bai · Junya Chen · Chang Liu · RUIYI (ROY) ZHANG · Georgiy Bobashev · Lawrence Carin -
2019 Oral: Stochastic Blockmodels meet Graph Neural Networks »
Nikhil Mehta · Lawrence Carin · Piyush Rai -
2019 Oral: Variational Annealing of GANs: A Langevin Perspective »
Chenyang Tao · Shuyang Dai · Liqun Chen · Ke Bai · Junya Chen · Chang Liu · RUIYI (ROY) ZHANG · Georgiy Bobashev · Lawrence Carin -
2018 Poster: Policy Optimization as Wasserstein Gradient Flows »
RUIYI (ROY) ZHANG · Changyou Chen · Chunyuan Li · Lawrence Carin -
2018 Poster: JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets »
Yunchen Pu · Shuyang Dai · Zhe Gan · Weiyao Wang · Guoyin Wang · Yizhe Zhang · Ricardo Henao · Lawrence Carin -
2018 Oral: Policy Optimization as Wasserstein Gradient Flows »
RUIYI (ROY) ZHANG · Changyou Chen · Chunyuan Li · Lawrence Carin -
2018 Oral: JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets »
Yunchen Pu · Shuyang Dai · Zhe Gan · Weiyao Wang · Guoyin Wang · Yizhe Zhang · Ricardo Henao · Lawrence Carin -
2018 Poster: Adversarial Time-to-Event Modeling »
Paidamoyo Chapfuwa · Chenyang Tao · Chunyuan Li · Courtney Page · Benjamin Goldstein · Lawrence Carin · Ricardo Henao -
2018 Oral: Adversarial Time-to-Event Modeling »
Paidamoyo Chapfuwa · Chenyang Tao · Chunyuan Li · Courtney Page · Benjamin Goldstein · Lawrence Carin · Ricardo Henao -
2018 Poster: Continuous-Time Flows for Efficient Inference and Density Estimation »
Changyou Chen · Chunyuan Li · Liquan Chen · Wenlin Wang · Yunchen Pu · Lawrence Carin -
2018 Poster: Chi-square Generative Adversarial Network »
Chenyang Tao · Liqun Chen · Ricardo Henao · Jianfeng Feng · Lawrence Carin -
2018 Poster: Variational Inference and Model Selection with Generalized Evidence Bounds »
Liqun Chen · Chenyang Tao · RUIYI (ROY) ZHANG · Ricardo Henao · Lawrence Carin -
2018 Oral: Chi-square Generative Adversarial Network »
Chenyang Tao · Liqun Chen · Ricardo Henao · Jianfeng Feng · Lawrence Carin -
2018 Oral: Continuous-Time Flows for Efficient Inference and Density Estimation »
Changyou Chen · Chunyuan Li · Liquan Chen · Wenlin Wang · Yunchen Pu · Lawrence Carin -
2018 Oral: Variational Inference and Model Selection with Generalized Evidence Bounds »
Liqun Chen · Chenyang Tao · RUIYI (ROY) ZHANG · Ricardo Henao · Lawrence Carin -
2017 Poster: Learning Hawkes Processes from Short Doubly-Censored Event Sequences »
Hongteng Xu · Dixin Luo · Hongyuan Zha -
2017 Poster: Stochastic Gradient Monomial Gamma Sampler »
Yizhe Zhang · Changyou Chen · Zhe Gan · Ricardo Henao · Lawrence Carin -
2017 Poster: Adversarial Feature Matching for Text Generation »
Yizhe Zhang · Zhe Gan · Kai Fan · Zhi Chen · Ricardo Henao · Dinghan Shen · Lawrence Carin -
2017 Talk: Adversarial Feature Matching for Text Generation »
Yizhe Zhang · Zhe Gan · Kai Fan · Zhi Chen · Ricardo Henao · Dinghan Shen · Lawrence Carin -
2017 Talk: Stochastic Gradient Monomial Gamma Sampler »
Yizhe Zhang · Changyou Chen · Zhe Gan · Ricardo Henao · Lawrence Carin -
2017 Talk: Learning Hawkes Processes from Short Doubly-Censored Event Sequences »
Hongteng Xu · Dixin Luo · Hongyuan Zha -
2017 Poster: Deep Generative Models for Relational Data with Side Information »
Changwei Hu · Piyush Rai · Lawrence Carin -
2017 Talk: Deep Generative Models for Relational Data with Side Information »
Changwei Hu · Piyush Rai · Lawrence Carin -
2017 Poster: Fake News Mitigation via Point Process Based Intervention »
Mehrdad Farajtabar · Jiachen Yang · Xiaojing Ye · Huan Xu · Rakshit Trivedi · Elias Khalil · Shuang Li · Le Song · Hongyuan Zha -
2017 Talk: Fake News Mitigation via Point Process Based Intervention »
Mehrdad Farajtabar · Jiachen Yang · Xiaojing Ye · Huan Xu · Rakshit Trivedi · Elias Khalil · Shuang Li · Le Song · Hongyuan Zha