158 citations · 259 across the 10 of their papers we have counts for
5 papers · 1 filter
Robust Reinforcement Learning with Wasserstein Constraint
Linfang Hou, Liang Pang, Xin Hong +3
Robust Reinforcement Learning aims to find the optimal policy with some extent of robustness to environmental dynamics. Existing learning algorithms usually enable the robustness t…
Target Transfer Q-Learning and Its Convergence Analysis
Yue Wang, Qi Meng, Wei Cheng +3
Q-learning is one of the most popular methods in Reinforcement Learning (RL). Transfer Learning aims to utilize the learned knowledge from source tasks to help new tasks to improve…
A Communication-Efficient Parallel Algorithm for Decision Tree
Qi Meng, Guolin Ke, Taifeng Wang +4
Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random Forest) is a widely used machine learning algorithm, due to its practical effectiveness and mo…
Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction
Qi Meng, Wei Chen, Jingcheng Yu +3
Regularized empirical risk minimization (R-ERM) is an important branch of machine learning, since it constrains the capacity of the hypothesis space and guarantees the generalizati…
Asynchronous Stochastic Gradient Descent with Delay Compensation
Shuxin Zheng, Qi Meng, Taifeng Wang +4
With the fast development of deep learning, it has become common to learn big neural networks using massive training data. Asynchronous Stochastic Gradient Descent (ASGD) is widely…