21 citations · 33 across the 5 of their papers we have counts for
5 papers
Are Large Language Models Really Robust to Word-Level Perturbations?
Haoyu Wang, Guozheng Ma, Cong Yu +10
The swift advancement in the scales and capabilities of Large Language Models (LLMs) positions them as promising tools for a variety of downstream tasks. In addition to the pursuit…
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning
Qin Zhang, Linrui Zhang, Haoran Xu +6
Offline safe RL is of great practical relevance for deploying agents in real-world applications. However, acquiring constraint-satisfying policies from the fixed dataset is non-tri…
A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning
Yuxing Wang, Tiantian Zhang, Yongzhe Chang +3
The integration of Reinforcement Learning (RL) and Evolutionary Algorithms (EAs) aims at simultaneously exploiting the sample efficiency as well as the diversity and robustness of…
Probability Density Estimation Based Imitation Learning
Yang Liu, Yongzhe Chang, Shilei Jiang +3
Imitation Learning (IL) is an effective learning paradigm exploiting the interactions between agents and environments. It does not require explicit reward signals and instead tries…
NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection
Liang Xu, Liying Zheng, Weijun Li +6
In recent studies, Lots of work has been done to solve time series anomaly detection by applying Variational Auto-Encoders (VAEs). Time series anomaly detection is a very common bu…