Mcdiarmid-type inequalities for graph-dependent variables and stability bounds
A crucial assumption in most statistical learning theory is that samples are independently
and identically distributed (iid). However, for many real applications, the iid assumption does …
and identically distributed (iid). However, for many real applications, the iid assumption does …
Tight and fast generalization error bound of graph embedding in metric space
Recent studies have experimentally shown that we can achieve in non-Euclidean metric
space effective and efficient graph embedding, which aims to obtain the vertices' …
space effective and efficient graph embedding, which aims to obtain the vertices' …
[PDF][PDF] Generalization bounds for knowledge graph embedding (trained by maximum likelihood)
We study theoretical properties of embedding methods for knowledge graph completion
under the" missing completely at random" assumption. We prove generalization error …
under the" missing completely at random" assumption. We prove generalization error …
Knowledge Graph Embedding: A Probabilistic Perspective and Generalization Bounds
We study theoretical properties of embedding methods for knowledge graph completion
under the missing completely at random assumption. We prove generalization error bounds …
under the missing completely at random assumption. We prove generalization error bounds …