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08/08/2023
Generalization bound for estimating causal effects from observational network data
Estimating causal effects from observational network data is a significa...
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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...
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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...
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02/10/2023
A Survey on Causal Reinforcement Learning
While Reinforcement Learning (RL) achieves tremendous success in sequent...
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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...
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08/09/2022
Long-term Causal Effects Estimation via Latent Surrogates Representation Learning
Estimating long-term causal effects based on short-term surrogates is a ...
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05/07/2022
Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance
Domain adaptation on time-series data is often encountered in the indust...
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01/13/2022
REST: Debiased Social Recommendation via Reconstructing Exposure Strategies
The recommendation system, relying on historical observational data to m...
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12/30/2021
Motif Graph Neural Network
Graphs can model complicated interactions between entities, which natura...
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11/18/2021
CCSL: A Causal Structure Learning Method from Multiple Unknown Environments
Most existing causal structure learning methods require data to be indep...
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11/14/2021
TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations
Sequential recommendation aims to choose the most suitable items for a u...
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11/05/2021
Transferable Time-Series Forecasting under Causal Conditional Shift
This paper focuses on the problem of semi-supervised domain adaptation f...
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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...
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06/14/2021
Graph Domain Adaptation: A Generative View
Recent years have witnessed tremendous interest in deep learning on grap...
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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...
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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...
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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...
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12/22/2020
Learning Disentangled Semantic Representation for Domain Adaptation
Domain adaptation is an important but challenging task. Most of the exis...
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12/22/2020
Semi-Supervised Disentangled Framework for Transferable Named Entity Recognition
Named entity recognition (NER) for identifying proper nouns in unstructu...
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12/22/2020
Time Series Domain Adaptation via Sparse Associative Structure Alignment
Domain adaptation on time series data is an important but challenging ta...
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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 ...
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09/19/2020
Causal Discovery with Multi-Domain LiNGAM for Latent Factors
Discovering causal structures among latent factors from observed data is...
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05/06/2020
TAG : Type Auxiliary Guiding for Code Comment Generation
Existing leading code comment generation approaches with the structure-t...
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11/30/2019
Disentanglement Challenge: From Regularization to Reconstruction
The challenge of learning disentangled representation has recently attra...
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10/13/2019
Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems
Data-driven models are becoming essential parts in modern mechanical sys...
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05/23/2019
Causal Discovery with Cascade Nonlinear Additive Noise Models
Identification of causal direction between a causal-effect pair from obs...
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11/16/2017
An Encoder-Decoder Framework Translating Natural Language to Database Queries
Machine translation is going through a radical revolution, driven by the...
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07/05/2017
SADA: A General Framework to Support Robust Causation Discovery with Theoretical Guarantee
Causation discovery without manipulation is considered a crucial problem...
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09/07/2016