Large Scale Machine Learning, Deep Learning, Reinforcement Learning
Specific Areas: Probabilistic Models, Bayesian Statistics, Applied Optimization
Professional Service: Invited Reviewer NIPS 2018
Recent Work (2016 - )
Deep Reinforcement Learning, Robotics
1. Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng. Learning to Explore via Meta-Policy Gradient (ICML 2018, paper, video)
2. Tianbing Xu, Qiang Liu, Jian Peng. Stochastic Variance Reduction for Policy Gradient Estimation (arXiv, video)
3. Tianbing Xu, Variational Inference for Policy Gradient (arXiv)
Large Scale Machine Learning
Highly Optimized LR algorithms and Distributed Training Systems in Spark (Video from 52min)
(Old, before 2015)
Selected Publications
1) Tianbing Xu, Jianfeng Gao, Lin Xiao, Amelia C. Regan, Online Classification Using a Voted RDA Method, 28th AAAI Conference on Artificial Intelligence, 2014 (AAAI 14, oral)
2) Xinran He, Junfeng Pan, Ou Jin,Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, Joaquin Quionero Candela. Practical Lessons from Predicting Clicks on Ads at Facebook, The 8th International Workshop on Data Mining for Online Advertising, co-located with KDD’2014 (ADKDD@KDD 14)
3) Tianbing Xu, Zhongfei Zhang, Philip Yu, and Bo Long. Generative Models for Evolutionary Clustering, ACM Transactions on Knowledge Discovery from Data (TKDD 12). 2012
4) Tianbing Xu, Alex Ihler. Multicore Gibbs Sampling for Unstructured, Dense Graphs, The fourteenth international conference on Artificial Intelligence and Statistics (AISTATS 2011)
5) Bo Long, Zhongfei (Mark) Zhang, Philip S. Yu, Tianbing Xu. Clustering on Complex Graphs, The 23nd National Conference on Artificial Intelligence, Chicago, Illinois, USA, 2008. (AAAI 08)