11 citations · 20 across the 6 of their papers we have counts for
6 papers
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling
Yunfan Li, Yiran Wang, Yu Cheng +1
Policy optimization methods are powerful algorithms in Reinforcement Learning (RL) for their flexibility to deal with policy parameterization and ability to handle model misspecifi…
On the Model-Misspecification in Reinforcement Learning
Yunfan Li, Lin Yang
The success of reinforcement learning (RL) crucially depends on effective function approximation when dealing with complex ground-truth models. Existing sample-efficient RL algorit…
Many-Objective Estimation of Distribution Optimization Algorithm Based on WGAN-GP
Zhenyu Liang, Yunfan Li, Zhongwei Wan
Estimation of distribution algorithms (EDA) are stochastic optimization algorithms. EDA establishes a probability model to describe the distribution of solution from the perspectiv…
Large Scale Many-Objective Optimization Driven by Distributional Adversarial Networks
Zhenyu Liang, Yunfan Li, Zhongwei Wan
Estimation of distribution algorithms (EDA) as one of the EAs is a stochastic optimization problem which establishes a probability model to describe the distribution of solutions a…
Horseshoe Regularization for Machine Learning in Complex and Deep Models
Anindya Bhadra, Jyotishka Datta, Yunfan Li +1
Since the advent of the horseshoe priors for regularization, global-local shrinkage methods have proved to be a fertile ground for the development of Bayesian methodology in machin…
Joint Mean-Covariance Estimation via the Horseshoe with an Application in Genomic Data Analysis
Yunfan Li, Jyotishka Datta, Bruce A. Craig +1
Seemingly unrelated regression is a natural framework for regressing multiple correlated responses on multiple predictors. The model is very flexible, with multiple linear regressi…