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Found 13 papers, 4 papers with code
Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy
As a key component to intuitive cognition and reasoning solutions in human intelligence, causal knowledge provides great potential for reinforcement learning (RL) agents' interpretability towards decision-making by helping reduce the searching space.
Deep Learning for Multivariate Time Series Imputation: A Survey
In this paper, we conduct a comprehensive survey on the recently proposed deep learning imputation methods.
Hierarchical Topological Ordering with Conditional Independence Test for Limited Time Series
Learning directed acyclic graphs (DAGs) to identify causal relations underlying observational data is crucial but also poses significant challenges.
TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences
assumption is often violated due to the inherent dependencies among the event sequences.
Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization
With contrastive learning, we propose a learning potential-guided metric for domain heterogeneity by promoting learning variant features.
Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences
Learning causal structure among event types from discrete-time event sequences is a particularly important but challenging task.
Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance
Based on this inspiration, we investigate the domain-invariant unweighted sparse associative structures and the domain-variant strengths of the structures.
gCastle: A Python Toolbox for Causal Discovery
$\texttt{gCastle}$ is an end-to-end Python toolbox for causal structure learning.
THP: Topological Hawkes Processes for Learning Causal Structure on Event Sequences
We further propose a causal structure learning method on THP in a likelihood framework.
An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks
Alarm root cause analysis is a significant component in the day-to-day telecommunication network maintenance, and it is critical for efficient and accurate fault localization and failure recovery.
Time Series Domain Adaptation via Sparse Associative Structure Alignment
To reduce the difficulty in the discovery of causal structure, we relax it to the sparse associative structure and propose a novel sparse associative structure alignment model for domain adaptation.
Mixed Set Domain Adaptation
Under the settings of MSDA, different categories of the source dataset are not all collected from the same domain(s).
An Improvement of PAA on Trend-Based Approximation for Time Series
In this paper, we propose two new approaches for time series that utilize approximate trend feature information.
