Jun Zhu
Assoc. Prof. @ THU
Adj. Faculty @ CMU
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The new code depository is at Group Github
MedLDA: Max-Margin Supervised Topic Models

This is a C++ implementation of the max-margin supervised topic models as presented in [1][2]. Click MedLDA for more information.

References

  1. Jun Zhu, Amr Ahmed, and Eric P. Xing. MedLDA: Maximum Margin Supervised Topic Models, Journal of Machine Learning Research, 13(Aug):2237--2278, 2012. (a short version was presented in ICML 2009)

  2. Qixia Jiang, Jun Zhu, Maosong Sun, and Eric P. Xing. Monte Carlo Methods for Maximum Margin Supervised Topic Models, In NIPS, Lake Tahoe, USA, 2012.

Gibbs MedLDA: a MedLDA model with fast sampling algorithms

This is a C++ implementation of the Gibbs max-margin supervised topic models as presented in [1]. Click Gibbs MedLDA for more information.

References

  1. Jun Zhu, Ning Chen, Hugh Perkins, and Bo Zhang. Gibbs Max-margin Topic Models with Fast Sampling Algorithms, In ICML, Atlanta, USA, 2013.

Sparse Topical Coding

This is a C++ implementation of the sparse topical coding as presented in [1]. Click STC for the code.

References

  1. Jun Zhu, and Eric P. Xing. Sparse Topical Coding, (Appendix), UAI 2011.

Nonparametric Bayesian Max-margin Matrix Factorization

This is a C++ implementation of the Nonparametric Bayesian max-margin matrix factorization [1][2]. Click iPM3F for more information.

References

  1. Minjie Xu, Jun Zhu, and Bo Zhang. Fast Max-Margin Matrix Factorization with Data Augmentation, In ICML, Atlanta, USA, 2013.

  2. Minjie Xu, Jun Zhu, and Bo Zhang. Bayesian Nonparametric Maximum Margin Matrix Factorization for Collaborative Prediction, In NIPS, Lake Tahoe, USA, 2012.

Online Bayesian Passive-Aggressive Learning

This is a C++ implementation of the Online Bayesian Passive-Aggressive Learning [1], including a streaming version of MedLDA and max-margin HDP. Click BayesPA for more information.

References

  1. Tianlin Shi, and Jun Zhu. Online Bayesian Passive Aggressive Learning, In ICML, 2014 (Full Version)

Distributed Sampler for Correlated Topic Models

This is a C++ implementation of the distributed Gibbs sampler for correlated topic models (CTMs) [1]. Click Scalable CTM for more information.

References

  1. Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng, and Bo Zhang. Scalable Inference for Logistic-Normal Topic Models, In NIPS, 2013.

Last updated on Dec. 4th, 2012.