Working with Fisher-Rao method part5(Machine Learning 2024)

Monodeep Mukherjee
1 min readApr 10, 2024
  1. Entropic mean-field min-max problems via Best Response and Fisher-Rao flows(arXiv)

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2.Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport (arXiv)

Author : Zihao Wang, Weizhi Fei, Hang Yin, Yangqiu Song, Ginny Y. Wong, Simon See

Abstract : Answering complex queries on knowledge graphs is important but particularly challenging because of the data incompleteness. Query embedding methods address this issue by learning-based models and simulating logical reasoning with set operators. Previous works focus on specific forms of embeddings, but scoring functions between embeddings are underexplored. In contrast to existing scoring functions motivated by local comparison or global transport, this work investigates the local and global trade-off with unbalanced optimal transport theory. Specifically, we embed sets as bounded measures in $\real$ endowed with a scoring function motivated by the Wasserstein-Fisher-Rao metric. Such a design also facilitates closed-form set operators in the embedding space. Moreover, we introduce a convolution-based algorithm for linear time computation and a block-diagonal kernel to enforce the trade-off. Results show that WFRE can outperform existing query embedding methods on standard datasets, evaluation sets with combinatorially complex queries, and hierarchical knowledge graphs. Ablation study shows that finding a better local and global trade-off is essential for performance improvement.

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Monodeep Mukherjee

Universe Enthusiast. Writes about Computer Science, AI, Physics, Neuroscience and Technology,Front End and Backend Development