Hao Yin

My research interests span a wide range within machine learning and data mining, including

network analysis: to develop concepts and algorithms that help understand and analyze structured data with complex relationship.
causal inference: to remove intrinsic bias and reduce variance in real-world data analysis and modeling with counterfactual reasoning.
general: modeling and optimization of machine learning problems and their analysis.

[Google Scholar]

Local Higher-Order Graph Clustering.
Hao Yin, Austin R. Benson, Jure Leskovec, David F. Gleich.
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017.
[paper] [slides @ KDD'17] [code] [data]
Higher-Order Clustering in Networks.
Hao Yin, Austin R. Benson, Jure Leskovec.
Physical Review E 97 (052306), 2018.
[paper] [code]
The Local Closure Coefficient: a New Perspective on Network Clustering.
Hao Yin, Austin R. Benson, Jure Leskovec.
ACM International Conference on Web Search and Data Mining (WSDM), 2019.
[paper] [slides @ WSDM'19]
Measuring Directed Triadic Closure with Closure Coefficients.
Hao Yin, Austin R. Benson, Johan Ugander.
To appear at Network Science, 2020+.
[paper] [poster @ NetSci'19]
A Simple Bipartite Graph Projection Model for Clustering in Networks.
Austin R. Benson*, Paul Liu*, Hao Yin*.
arXiv:2007.00761, 2020+.
[paper]

Randomized Graph Cluster Randomization.
Johan Ugander*, Hao Yin*.
arXiv:2009.02297, 2020+.
[paper]

Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions.
Yichen Chen*, Dongdong Ge*, Mengdi Wang*, Zizhuo Wang*, Yinyu Ye*, Hao Yin*.
International Conference on Machine Learning (ICML), 2017.
[paper] [Supplementary]
Road User Interaction Prediction.
James Guo, Jiyang Gao, Hao Yin.
In Preparation, 2020+.