2 citations · 6 across the 18 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2024
On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization
Ling Liang, Haizhao Yang
We consider a regularized expected reward optimization problem in the non-oblivious setting that covers many existing problems in reinforcement learning (RL). In order to solve suc…
cs.LG2022★ 1 cited
Accelerating nuclear-norm regularized low-rank matrix optimization through Burer-Monteiro decomposition
Ching-pei Lee, Ling Liang, Tianyun Tang +1
This work proposes a rapid algorithm, BM-Global, for nuclear-norm-regularized convex and low-rank matrix optimization problems. BM-Global efficiently decreases the objective value…