Fetching the paper…
Reading the bibliography…
Machine learning models based on temporal point processes are the state of the art in a wide variety of applications involving discrete events in continuous time.
Individual choice behavior: A theoretical analysis
R Duncan Luce · 1959
Earlier work this paper cites.
Spectra of some self-exciting and mutually exciting point processes
Alan G Hawkes · 1971
Earlier work this paper cites.
A cluster process representation of a self-exciting process
Alan G Hawkes and David Oakes · 1974
Earlier work this paper cites.
Simulation of nonhomogeneous poisson processes by thinning
PA W Lewis and Gerald S Shedler · 1979
Earlier work this paper cites.
Poisson processes
John Frank Charles Kingman · 1992
Earlier work this paper cites.
The mathematics of infectious diseases
Herbert W Hethcote · 2000
Earlier work this paper cites.
Models, reasoning and inference
Judea Pearl et al · 2000
Earlier work this paper cites.
Mental models and counterfactual thoughts about what might have been
Ruth MJ Byrne · 2002
Earlier work this paper cites.
An introduction to the theory of point processes: volume I: elementary theory and methods
Daryl J Daley and David Vere-Jones · 2003
Earlier work this paper cites.
A note on generation times in epidemic models
Åke Svensson · 2007
Earlier work this paper cites.
The functional theory of counterfactual thinking
Kai Epstude and Neal J Roese · 2008
Earlier work this paper cites.
Statistical modeling of causal effects in continuous time
Judith J Lok · 2008
Earlier work this paper cites.
Causality
Judea Pearl · 2009
Earlier work this paper cites.
Counterfactual analyses with graphical models based on local independence
Kjetil Røysland · 2012
Earlier work this paper cites.
Discovering latent network structure in point process data
Scott Linderman and Ryan Adams · 2014
Earlier work this paper cites.
Ebola virus disease in west africa—the first 9 months of the epidemic and forward projections
WHO Ebola Response Team · 2014
Earlier work this paper cites.
Causal reasoning for events in continuous time: A decision-theoretic approach
Vanessa Didelez · 2015
Earlier work this paper cites.
Causal inference in statistics, social, and biomedical sciences
Guido W Imbens and Donald B Rubin · 2015
Earlier work this paper cites.
Learning granger causality for hawkes processes
Hongteng Xu, Mehrdad Farajtabar, and Hongyuan Zha · 2016
Cited alongside, same era.
A hawkes’ eye view of network information flow
Michael G Moore and Mark A Davenport · 2016
Cited alongside, same era.
Coevolve: A joint point process model for information diffusion and network evolution
Mehrdad Farajtabar, Yichen Wang, Manuel Gomez-Rodriguez, Shuang Li, Hongyuan Zha, and Le Song · 2017
Cited alongside, same era.
Steering social activity: A stochastic optimal control point of view
Ali Zarezade, Abir De, Utkarsh Upadhyay, Hamid R Rabiee, and Manuel Gomez-Rodriguez · 2017
Cited alongside, same era.
Uncovering causality from multivariate hawkes integrated cumulants
Massil Achab, Emmanuel Bacry, Stéphane Gaıffas, Iacopo Mastromatteo, and Jean-François Muzy · 2017
Cited alongside, same era.
Reliable decision support using counterfactual models
Peter Schulam and Suchi Saria · 2017
Enhancing human learning via spaced repetition optimization
Behzad Tabibian, Utkarsh Upadhyay, Abir De, Ali Zarezade, Bernhard Schölkopf, and Manuel Gomez-Rodriguez · 2019
Later among the works it cites.
Counterfactual off-policy evaluation with gumbel-max structural causal models
Michael Oberst and David Sontag · 2019
Later among the works it cites.
The challenges of modeling and forecasting the spread of covid-19
Andrea L Bertozzi, Elisa Franco, George Mohler, Martin B Short, and Daniel Sledge · 2020
Later among the works it cites.
Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of sars-cov-2 in different settings: a mathematical modelling study
Adam J Kucharski, Petra Klepac, Andrew JK Conlan, Stephen M Kissler, Maria L Tang, Hannah Fry, Julia R Gog, W John Edmunds, Jon C Emery, Graham Medley, et al · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Gumbel machinery, Jan 2017
Chris J. Maddison and Danny Tarlow · 2017
Cited alongside, same era.
Heterogeneities in the case fatality ratio in the west african ebola outbreak 2013–2016
Tini Garske, Anne Cori, Archchun Ariyarajah, Isobel M Blake, Ilaria Dorigatti, Tim Eckmanns, Christophe Fraser, Wes Hinsley, Thibaut Jombart, Harriet L Mills, et al · 2017
Cited alongside, same era.
High resolution global gridded data for use in population studies
Christopher T Lloyd, Alessandro Sorichetta, and Andrew J Tatem · 2017
Cited alongside, same era.
Learning with temporal point processes
M Gomez Rodriguez and Isabel Valera · 2018
Cited alongside, same era.
Deep reinforcement learning of marked temporal point processes
Utkarsh Upadhyay, Abir De, and Manuel Gomez-Rodriguez · 2018
Cited alongside, same era.
Lars Lorch, Heiner Kremer, William Trouleau, Stratis Tsirtsis, Aron Szanto, Bernhard Schölkopf, and Manuel Gomez-Rodriguez · 2020
Later among the works it cites.
Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak
Chad R Wells, Pratha Sah, Seyed M Moghadas, Abhishek Pandey, Affan Shoukat, Yaning Wang, Zheng Wang, Lauren A Meyers, Burton H Singer, and Alison P Galvani · 2020
Later among the works it cites.
Cause: Learning granger causality from event sequences using attribution methods
Wei Zhang, Thomas Panum, Somesh Jha, Prasad Chalasani, and David Page · 2020
Later among the works it cites.
Time-dependent mediators in survival analysis: Modeling direct and indirect effects with the additive hazards model
Odd O Aalen, Mats J Stensrud, Vanessa Didelez, Rhian Daniel, Kjetil Røysland, and Susanne Strohmaier · 2020
Later among the works it cites.
Causal inference in continuous time: an example on prostate cancer therapy
Pål Christie Ryalen, Mats Julius Stensrud, Sophie Fosså, and Kjetil Røysland · 2020
Later among the works it cites.
Neural temporal point processes: A review
Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, and Stephan Günnemann · 2021
Closest in time.
Mobility network models of covid-19 explain inequities and inform reopening
Serina Chang, Emma Pierson, Pang Wei Koh, Jaline Gerardin, Beth Redbird, David Grusky, and Jure Leskovec · 2021
Closest in time.
Thp: Topological hawkes processes for learning granger causality on event sequences
Ruichu Cai, Siyu Wu, Jie Qiao, Zhifeng Hao, Keli Zhang, and Xi Zhang · 2021
Closest in time.
Cumulants of hawkes processes are robust to observation noise
William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, and Patrick Thiran · 2021
Closest in time.
A variational inference approach to learning multivariate wold processes
Jalal Etesami, William Trouleau, Negar Kiyavash, Matthias Grossglauser, and Patrick Thiran · 2021
Closest in time.
Causal inference for event pairs in multivariate point processes
Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Xiao Shou, Nicholas Mattei, and Kristin P Bennett · 2021
Closest in time.
Counterfactual explanations in sequential decision making under uncertainty
Stratis Tsirtsis, Abir De, and Manuel Gomez-Rodriguez · 2021
Closest in time.
A review of the gumbel-max trick and its extensions for discrete stochasticity in machine learning
Iris AM Huijben, Wouter Kool, Max Benedikt Paulus, and Ruud JG Van Sloun · 2022
Closest in time.