2 citations · 4 across the 3 of their papers we have counts for
4 papers
Fast Rates for Offline Q-Learning with KL Regularization
Yize Hao, Guangxin Jiang, Shuang Qiu
This paper investigates offline Q-learning for finite-horizon Markov decision processes with Kullback-Leibler (KL) regularization. Recent studies have shown that KL regularization…
New Bounds and Truncation Boundaries for Importance Sampling
Yijuan Liang, Guangxin Jiang, Michael C. Fu
Importance sampling (IS) is a technique that enables statistical estimation of output performance at multiple input distributions from a single nominal input distribution. IS is co…
An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development
Jin Yang, Guangxin Jiang, Yinan Wang +1
Recent years have witnessed exponential growth in developing deep learning (DL) models for time-series electricity forecasting in power systems. However, most of the proposed model…
Adaptive Importance Sampling for Efficient Stochastic Root Finding and Quantile Estimation
Shengyi He, Guangxin Jiang, Henry Lam +1
In solving simulation-based stochastic root-finding or optimization problems that involve rare events, such as in extreme quantile estimation, running crude Monte Carlo can be proh…