151 citations · 542 across the 33 of their papers we have counts for
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Policy Optimization by Genetic Distillation
Tanmay Gangwani, Jian Peng
Genetic algorithms have been widely used in many practical optimization problems. Inspired by natural selection, operators, including mutation, crossover and selection, provide eff…
Efficient Localized Inference for Large Graphical Models
Jinglin Chen, Jian Peng, Qiang Liu
We propose a new localized inference algorithm for answering marginalization queries in large graphical models with the correlation decay property. Given a query variable and a lar…
Energy-efficient Amortized Inference with Cascaded Deep Classifiers
Jiaqi Guan, Yang Liu, Qiang Liu +1
Deep neural networks have been remarkable successful in various AI tasks but often cast high computation and energy cost for energy-constrained applications such as mobile sensing.…
Action-depedent Control Variates for Policy Optimization via Stein's Identity
Hao Liu, Yihao Feng, Yi Mao +3
Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems. However, it still often suffers from the large variance issue on…
Stochastic Variance Reduction for Policy Gradient Estimation
Tianbing Xu, Qiang Liu, Jian Peng
Recent advances in policy gradient methods and deep learning have demonstrated their applicability for complex reinforcement learning problems. However, the variance of the perform…
Empower Sequence Labeling with Task-Aware Neural Language Model
Liyuan Liu, Jingbo Shang, Frank F. Xu +4
Linguistic sequence labeling is a general modeling approach that encompasses a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in…
Stein Variational Policy Gradient
Yang Liu, Prajit Ramachandran, Qiang Liu +1
Policy gradient methods have been successfully applied to many complex reinforcement learning problems. However, policy gradient methods suffer from high variance, slow convergence…