Welcome! I am a PhD student in the Machine Learning Department at the
Carnegie Mellon University, advised by Eric Xing and Pradeep Ravikumar.
I am interested in all aspects of probabilistic models -- including inference,
parameter learning, structure learning, and connections with neural networks.
- DAGs with NO TEARS: Continuous Optimization for Structure Learning
Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric Xing.
Advances in Neural Information Processing Systems, 2018. (Spotlight)
- State Space LSTM Models with Particle MCMC Inference
Xun Zheng, Manzil Zaheer, Amr Ahmed, Yuan Wang, Eric Xing, Alex Smola.
arXiv preprint arXiv:1711.11179, 2017.
- Linear Time Samplers for Supervised Topic Models using Compositional Proposals
Xun Zheng, Yaoliang Yu, Eric Xing.
Conference on Knowledge Discovery and Data Mining, 2015.
- Minimizing Nonconvex Non-Separable Functions
Yaoliang Yu, Xun Zheng, Micol Marchetti-Bowick, Eric Xing.
International Conference on Artificial Intelligence and Statistics, 2015.
- LightLDA: Big Topic Models on Modest Compute Clusters
Jinhui Yuan, Fei Gao, Qirong Ho, Wei Dai, Jinliang Wei, Xun Zheng, Eric Xing, Tie-Yan Liu, Wei-Ying Ma.
International World Wide Web Conference, 2015.
- Scalable Inference for Logistic-Normal Topic Models
Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng, Bo Zhang.
Advances in Neural Information Processing Systems, 2013.