Ji Zhu

Affiliations:
  • University of Michigan, Department of Statistics, Ann Arbor, MI, USA
  • Stanford University, Department of Statistics, CA, USA (PhD 2003)


According to our database1, Ji Zhu authored at least 20 papers between 2001 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2024
Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks.
J. Mach. Learn. Res., 2024

2023
Community models for networks observed through edge nominations.
J. Mach. Learn. Res., 2023

Link Prediction for Egocentrically Sampled Networks.
J. Comput. Graph. Stat., 2023

Fair Information Spread on Social Networks with Community Structure.
CoRR, 2023

2020
Detecting Overlapping Communities in Networks Using Spectral Methods.
SIAM J. Math. Data Sci., 2020

High-dimensional Gaussian graphical models on network-linked data.
J. Mach. Learn. Res., 2020

2019
High-dimensional Gaussian graphical model for network-linked data.
CoRR, 2019

2018
Link prediction for egocentrically sampled networks.
CoRR, 2018

2015
Community Detection in Networks with Node Features.
CoRR, 2015

2013
Link prediction for partially observed networks
CoRR, 2013

2012
Sparse Ising Models with Covariates
CoRR, 2012

2011
On Consistency of Community Detection in Networks
CoRR, 2011

2004
Boosting as a Regularized Path to a Maximum Margin Classifier.
J. Mach. Learn. Res., 2004

The Entire Regularization Path for the Support Vector Machine.
J. Mach. Learn. Res., 2004

A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

2003
1-norm Support Vector Machines.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Margin Maximizing Loss Functions.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Boosting and support vector machines as optimal separators.
Proceedings of the Document Recognition and Retrieval X, 2003

2002
Support Vector Machines, Kernel Logistic Regression and Boosting.
Proceedings of the Multiple Classifier Systems, Third International Workshop, 2002

2001
Kernel Logistic Regression and the Import Vector Machine.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001


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