Hawkes Processes

Latest papers

14 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Projected Wasserstein-Fisher-Rao Flows for Hawkes Background-Intensity Estimation
Oct 14, 2026Isaias Banales, Arturo JaramilloHawkes Processes Temporal Point Processes
Facultad de Ciencias, Universidad Aut´onoma de M´exico · Department of Probability and Statistics, Centro de Investigaci´on en Matem´aticas A.C., Guanajuato, M´exico
Reinforcement Learning with Hawkes-Structured Networks for Market Making
Oct 12, 2026Fernando Acero, Philipp Plank, Yufei ZhangHawkes Processes Non-Markovian RL
Department of Mathematics, Imperial College London, United Kingdom
Exact Phase-Type Point Processes for Certified Measurement and Prediction of Event Streams
Oct 10, 2026Sayak DuttaHawkes Processes Temporal Point Processes
When Should a Human Take Back Control? Optimal Delegation under Turbulent AI Risk
Sep 25, 2026Haoze Yan, Julien Roze, Ved Upadhyay +2Hawkes Processes Human Oversight of AI
Department of Industrial Engineering and Operations Research, UC Berkeley, United States. · Stoa, United States. · Department of Cybersecurity, University at Albany, SUNY, United States.
Robust Market Making with Hawkes Order Flow and Price Impact via Adversarial Reinforcement Learning
Sep 19, 2026Hao Yang, Zhenguo XuAdversarial MARL Hawkes Processes
North China Institute of Computer System Engineering, Beijing, China · University of Science and Technology of China, Hefei, China
NeuroMem-FHP: A Likelihood-Free Deep Learning Framework for Parameter Estimation of Fractional Hawkes Process
Jul 13, 2026Neha Gupta, Aditya MaheshwariTemporal Point Processes Parameter Estimation
Operations Management and Quantitative Techniques Area, Indian Institute of Management Indore, Indore 453556, India.
HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation
May 21, 2026Zewei Deng, Tinghan Ye, Liyan XieMemory-Augmented Language Model Agents Hawkes Processes
Department of Industrial and Systems Engineering, University of Minnesota · H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
On Observation Time for Recovering Latent Hawkes Networks
May 8, 2026Jonas Linkerhägner, Michele Bortolasi, Lorenzo Baldassari +2Temporal Point Processes Sample Complexity
University of Basel · Rice University
FASE : A Fairness-Aware Spatiotemporal Event Graph Framework for Predictive Policing
Apr 19, 2026Pronob Kumar Barman, Pronoy Kumar Barman, Plaban Kumar Barman +1Algorithmic Bias Group Fairness
Department of Information Systems University of Maryland, Baltimore County Baltimore, MD, USA · Department of Statistics Jagannath University Dhaka, Bangladesh · MBBS Shaheed M. Monsur Ali Medical College Sirajganj, Bangladesh
Massively Parallel Exact Inference for Hawkes Processes
Apr 1, 2026Ahmer Raza, Hudson SmithHawkes Processes Temporal Point Processes
Clemson University
Can LLM Agents Simulate Dynamic Networks? A Case Study on Email Networks with Phishing Synthesis
Mar 20, 2026Siqi Miao, Ziyang Chen, Yuhong Luo +4Hawkes Processes Large Language Model-Based Social Simulation
Georgia Institute of Technology · University of Maryland, College Park · Rutgers University
HawkesRank: Event-Driven Centrality for Real-Time Importance Ranking
Mar 12, 2026Didier Sornette, Yishan Luo, Sandro Claudio LeraHawkes Processes Social Network Analysis
Institute of Risk Analysis, Prediction and Management, Southern University of Science and Technology, Shenzhen, China · Warwick Business School, University of Warwick, Coventry, UK
Stability of AI Governance Systems: A Coupled Dynamics Model of Public Trust and Social Disruptions
Mar 10, 2026Jiaqi Lai, Hou Liang, Weihong HuangHawkes Processes AI Governance
Nanyang Technological University, Singapore
How Predicted Links Influence Network Evolution: Disentangling Choice and Algorithmic Feedback in Dynamic Graphs
Mar 4, 2026Mathilde Perez, Raphaël Romero, Jefrey Lijffijt +1Hawkes Processes Social Network Analysis
LTCI, Télécom Paris, Institut Polytechnique de Paris, France. · IDLab , Ghent University , Belgium.

Oct 14, 2026J/K move · Enter open · S save

Projected Wasserstein-Fisher-Rao Flows for Hawkes Background-Intensity Estimation

Isaias Banales, Arturo Jaramillo

Facultad de Ciencias, Universidad Aut´onoma de M´exico · Department of Probability and Statistics, Centro de Investigaci´on en Matem´aticas A.C., Guanajuato, M´exico

Hawkes Processes Temporal Point Processes Wasserstein Gradient Flows

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We consider the problem of estimating the background intensity of a Hawkes process when the triggering mechanism is known, treating the background as an unknown finite positive measure with a regular density. We propose a likelihood-based variational approach based on the Wasserstein-Fisher-Rao geometry. In this setting, the relevant first variations are naturally distribution-valued and therefore fall outside the classical gradient-flow formulation. We develop a distributional framework for the resulting dynamics and give them a finite-dimensional meaning through projections onto statistical manifolds. We identify the true background as the unique zero of a limiting generalized gradient and, under suitable stability, compactness, integrability and approximation conditions, establish a conditional consistency theorem for measurable estimators satisfying the empirical projected equations on events whose probabilities tend to one. Finally, we specialize the construction to Gaussian-mixture manifolds and obtain an explicit finite-dimensional scheme.