Estimating causal effects from observational network data is a significa...
Learning Granger causality from event sequences is a challenging but
ess...
Learning causal structure among event types from discrete-time event
seq...
While Reinforcement Learning (RL) achieves tremendous success in sequent...
Explainability of Graph Neural Networks (GNNs) is critical to various GN...
UAVs (Unmanned Aerial Vehicles) dynamic encirclement is an emerging fiel...
Online path planning for multiple unmanned aerial vehicle (multi-UAV) sy...
Graphs can model complicated interactions between entities, which natura...
Most existing causal structure learning methods require data to be
indep...
The Text-to-SQL task, aiming to translate the natural language of the
qu...
Causal discovery from observational data is an important but challenging...
Learning Granger causality among event types on multi-type event sequenc...
We consider the problem of estimating a particular type of linear
non-Ga...
Domain adaptation is an important but challenging task. Most of the exis...
Named entity recognition (NER) for identifying proper nouns in unstructu...
Causal discovery aims to recover causal structures or models underlying ...
Discovering causal structures among latent factors from observed data is...
A rework network is a common manufacturing system, in which flows (produ...
Identification of causal direction between a causal-effect pair from obs...
Causation discovery without manipulation is considered a crucial problem...
Differential privacy enables organizations to collect accurate aggregate...