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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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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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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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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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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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06/28/2020
DeepACC:Automate Chromosome Classification based on Metaphase Images using Deep Learning Framework Fused with Prior Knowledge
Chromosome classification is an important but difficult and tedious task...
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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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05/23/2019