Department of Computer Science and Software Engineering
3101P Shelby Center for Engineering Technology
Auburn, AL 36849-5347
boliu AT auburn DOT edu
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization.
B. Liu*, T. Xie* (* equal contribution), Y. Xu, M. Ghavamzadeh, Y. Chow, D. Lyu, D. Yoon
32nd Conference on Neural Information Processing Systems (NIPS), Montreal, CA, 2018
The first risk-sensitive policy search algorithm with single time-scale and sample complexity analysis. It is also the first time introducing coordinate descent/ascent formulation into Reinforcement Learning
Uncorrelated Group LASSO.
D Kong, J Liu, B. Liu, X Bao.
30th AAAI Conference on Artificial Intelligence (AAAI), Phoenix, AZ, Feb 12-17, 2016
Finite-Sample Analysis of Proximal Gradient TD Algorithms.
B. Liu, J Liu, M Ghavamzadeh, S Mahadevan, M Petrik.
31st Conference on Uncertainty in Artificial Intelligence (UAI), Amsterdam, The Netherlands, July 12-16, 2015, Facebook Best Student Paper Award. [ppt] [video]
The first paper giving sample complexity analysis of RL algorithms with linear computational cost per step.
Regularized Off-Policy TD-Learning.
B. Liu, S Mahadevan, J Liu.
26th Annual Conference on Neural Information Processing Systems (NIPS),
Lake Tahoe, Nevada, 2012, December 3-6, Spotlight Presentation (5% acceptance). [ppt] [video]
The first paper introducing saddle-point formulation into TD learning and Reinforcement Learning