
On the Role of Entropybased Loss for Learning Causal Structures with Continuous Optimization
Causal discovery from observational data is an important but challenging...
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THP: Topological Hawkes Processes for Learning Granger Causality on Event Sequences
Learning Granger causality among event types on multitype event sequenc...
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FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders
We consider the problem of estimating a particular type of linear nonGa...
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Learning Disentangled Semantic Representation for Domain Adaptation
Domain adaptation is an important but challenging task. Most of the exis...
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SemiSupervised Disentangled Framework for Transferable Named Entity Recognition
Named entity recognition (NER) for identifying proper nouns in unstructu...
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Generalized Independent Noise Condition for Estimating Linear NonGaussian Latent Variable Graphs
Causal discovery aims to recover causal structures or models underlying ...
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Causal Discovery with MultiDomain LiNGAM for Latent Factors
Discovering causal structures among latent factors from observed data is...
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General MultiState Rework Network and Reliability Algorithm
A rework network is a common manufacturing system, in which flows (produ...
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Causal Discovery with Cascade Nonlinear Additive Noise Models
Identification of causal direction between a causaleffect pair from obs...
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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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Convex Optimization for Linear Query Processing under Approximate Differential Privacy
Differential privacy enables organizations to collect accurate aggregate...
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Zhifeng Hao
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