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20182022
most citedA Graph Autoencoder Approach to Causal Structure Learning

55 citations · 93 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.LG202234 cited

STICC: A multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity

Yuhao Kang, Kunlin Wu, Song Gao +5

Spatial clustering has been widely used for spatial data mining and knowledge discovery. An ideal multivariate spatial clustering should consider both spatial contiguity and aspati…

cs.LG20224 cited

Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions

Ignavier Ng, Yujia Zheng, Jiji Zhang +1

Many of the causal discovery methods rely on the faithfulness assumption to guarantee asymptotic correctness. However, the assumption can be approximately violated in many ways, le…

cs.LG2020

On the Role of Sparsity and DAG Constraints for Learning Linear DAGs

Ignavier Ng, AmirEmad Ghassami, Kun Zhang

Learning graphical structures based on Directed Acyclic Graphs (DAGs) is a challenging problem, partly owing to the large search space of possible graphs. A recent line of work for…

cs.LG201955 cited

A Graph Autoencoder Approach to Causal Structure Learning

Ignavier Ng, Shengyu Zhu, Zhitang Chen +1

Causal structure learning has been a challenging task in the past decades and several mainstream approaches such as constraint- and score-based methods have been studied with theor…

cs.LG2019

Causal Discovery with Reinforcement Learning

Shengyu Zhu, Ignavier Ng, Zhitang Chen

Discovering causal structure among a set of variables is a fundamental problem in many empirical sciences. Traditional score-based casual discovery methods rely on various local he…