Cover Image

Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wu, Dongxia, Idé, Tsuyoshi, Lozano, Aurélie, Kollias, Georgios, Navrátil, Jiří, Abe, Naoki, Ma, Yi-An, Yu, Rose
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Cardinality-Regularized Hawkes-Granger Model
by: Idé, Tsuyoshi, et al.
Published: (2022)

Black-Box Anomaly Attribution
by: Idé, Tsuyoshi, et al.
Published: (2023)

Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length
by: Hlavackova-Schindler, Katerina, et al.
Published: (2023)

Targeted Advertising on Social Networks Using Online Variational Tensor Regression
by: Idé, Tsuyoshi, et al.
Published: (2022)

Larimar: Large Language Models with Episodic Memory Control
by: Das, Payel, et al.
Published: (2024)

Cross-Process Defect Attribution using Potential Loss Analysis
by: Idé, Tsuyoshi, et al.
Published: (2025)

Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs
by: Niu, Ruijia, et al.
Published: (2024)

Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
by: Niu, Ruijia, et al.
Published: (2024)

Can Memory-Augmented Language Models Generalize on Reasoning-in-a-Haystack Tasks?
by: Das, Payel, et al.
Published: (2025)

Divide and Learn: Multi-Objective Combinatorial Optimization at Scale
by: Singh, Esha, et al.
Published: (2026)

NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models
by: Dhurandhar, Amit, et al.
Published: (2024)

Granger Causality in Extremes
by: Bodik, Juraj, et al.
Published: (2024)

Stationary Processes, Wiener-Granger Causality, and Matrix Spectral Factorization
by: Ephremidze, Lasha
Published: (2024)

Diffusion-BBO: Diffusion-Based Inverse Modeling for Online Black-Box Optimization
by: Wu, Dongxia, et al.
Published: (2024)

Learning to Transform for Generalizable Instance-wise Invariance
by: Singhal, Utkarsh, et al.
Published: (2023)

Jacobian Regularizer-based Neural Granger Causality
by: Zhou, Wanqi, et al.
Published: (2024)

Towards Uncertainty-Aware Federated Granger Causal Learning
by: Mohanty, Ayush, et al.
Published: (2026)

Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis
by: Idé, Tsuyoshi, et al.
Published: (2024)

SimulRAG: Simulator-based RAG for Grounding LLMs in Long-form Scientific QA
by: Xu, Haozhou, et al.
Published: (2025)

Granger Causality using Neural Networks
by: Sultan, Malik Shahid, et al.
Published: (2022)

Transformers with Sparse Attention for Granger Causality
by: Mahesh, Riya, et al.
Published: (2024)

Statistical Inference for Local Granger Causality
by: Liu, Yan, et al.
Published: (2021)

Granger Causality Maps for Langevin Systems
by: Barnett, Lionel, et al.
Published: (2025)

Quantile Granger Causality in the Presence of Instability
by: Mayer, Alexander, et al.
Published: (2024)

Granger Causal Interaction Skill Chains
by: Chuck, Caleb, et al.
Published: (2023)

Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networks
by: Jin, Songyao, et al.
Published: (2025)

Granger Causality Detection with Kolmogorov-Arnold Networks
by: Lin, Hongyu, et al.
Published: (2024)

High-Dimensional Granger Causality for Climatic Attribution
by: Friedrich, Marina, et al.
Published: (2023)

Improving Transformers using Faithful Positional Encoding
by: Idé, Tsuyoshi, et al.
Published: (2024)

Federated Granger Causality Learning for Interdependent Clients with State Space Representation
by: Mohanty, Ayush, et al.
Published: (2025)

GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Imaging Studies
by: Nguyen, Minh, et al.
Published: (2022)

From News to Returns: A Granger-Causal Hypergraph Transformer on the Sphere
by: Harit, Anoushka, et al.
Published: (2025)

Generation Constraint Scaling Can Mitigate Hallucination
by: Kollias, Georgios, et al.
Published: (2024)

Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles
by: Adedayo, S. A.
Published: (2025)

MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
by: Eckmann, Peter, et al.
Published: (2024)

Discovering Mixtures of Structural Causal Models from Time Series Data
by: Varambally, Sumanth, et al.
Published: (2023)

Instance-wise Supervision-level Optimization in Active Learning
by: Matsuo, Shinnosuke, et al.
Published: (2025)

Granger Causality in Expectiles: an M-vine copula test
by: Fuentes-Martínez, Roberto, et al.
Published: (2026)

Bayesian Testing Of Granger Causality In Functional Time Series
by: Sen, Rituparna, et al.
Published: (2021)

Granger Causality in High-Dimensional Networks of Time Series
by: Aslan, Sipan, et al.
Published: (2024)