research
∙
08/08/2023
Generalization bound for estimating causal effects from observational network data
Estimating causal effects from observational network data is a significa...
share
research
∙
06/25/2023
TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences
Learning Granger causality from event sequences is a challenging but ess...
research
∙
05/10/2023
Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences
Learning causal structure among event types from discrete-time event seq...
research
∙
02/10/2023
A Survey on Causal Reinforcement Learning
While Reinforcement Learning (RL) achieves tremendous success in sequent...
research
∙
12/14/2022
On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach
Explainability of Graph Neural Networks (GNNs) is critical to various GN...
research
∙
06/17/2022
VGSwarm: A Vision-based Gene Regulation Network for UAVs Swarm Behavior Emergence
UAVs (Unmanned Aerial Vehicles) dynamic encirclement is an emerging fiel...
research
∙
03/05/2022
Vision-based Distributed Multi-UAV Collision Avoidance via Deep Reinforcement Learning for Navigation
Online path planning for multiple unmanned aerial vehicle (multi-UAV) sy...
research
∙
12/30/2021
Motif Graph Neural Network
Graphs can model complicated interactions between entities, which natura...
research
∙
11/18/2021
CCSL: A Causal Structure Learning Method from Multiple Unknown Environments
Most existing causal structure learning methods require data to be indep...
research
∙
11/01/2021
SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL
The Text-to-SQL task, aiming to translate the natural language of the qu...
research
∙
06/05/2021
On the Role of Entropy-based Loss for Learning Causal Structures with Continuous Optimization
Causal discovery from observational data is an important but challenging...
research
∙
05/23/2021
THP: Topological Hawkes Processes for Learning Granger Causality on Event Sequences
Learning Granger causality among event types on multi-type event sequenc...
research
∙
03/26/2021
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders
We consider the problem of estimating a particular type of linear non-Ga...
research
∙
12/22/2020
Learning Disentangled Semantic Representation for Domain Adaptation
Domain adaptation is an important but challenging task. Most of the exis...
research
∙
12/22/2020
Semi-Supervised Disentangled Framework for Transferable Named Entity Recognition
Named entity recognition (NER) for identifying proper nouns in unstructu...
research
∙
10/10/2020
Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs
Causal discovery aims to recover causal structures or models underlying ...
research
∙
09/19/2020
Causal Discovery with Multi-Domain LiNGAM for Latent Factors
Discovering causal structures among latent factors from observed data is...
research
∙
12/06/2019
General Multi-State Rework Network and Reliability Algorithm
A rework network is a common manufacturing system, in which flows (produ...
research
∙
05/23/2019
Causal Discovery with Cascade Nonlinear Additive Noise Models
Identification of causal direction between a causal-effect pair from obs...
research
∙
07/05/2017
SADA: A General Framework to Support Robust Causation Discovery with Theoretical Guarantee
Causation discovery without manipulation is considered a crucial problem...
research
∙
02/13/2016
