1 citations · 2 across the 6 of their papers we have counts for
6 papers
Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networks
Rujia Shen, Boran Wang, Chao Zhao +2
Causal discovery from time-series data aims to capture both intra-slice (contemporaneous) and inter-slice (time-lagged) causality between variables within the temporal chain, which…
ProSpec RL: Plan Ahead, then Execute
Liangliang Liu, Yi Guan, BoRan Wang +5
Imagining potential outcomes of actions before execution helps agents make more informed decisions, a prospective thinking ability fundamental to human cognition. However, mainstre…
FiVTS: Time Series Forecasting Via Capturing Intra- and Inter-Variable Variations in the Frequency Domain
Rujia Shen, Yang Yang, Yaoxion Lin +4
Time series forecasting (TSF) plays a crucial role in various applications, including medical monitoring and crop growth. Despite the advancements in deep learning methods for TSF,…
Causal prompting model-based offline reinforcement learning
Xuehui Yu, Yi Guan, Rujia Shen +3
Model-based offline Reinforcement Learning (RL) allows agents to fully utilise pre-collected datasets without requiring additional or unethical explorations. However, applying mode…
Blood Glucose Control Via Pre-trained Counterfactual Invertible Neural Networks
Jingchi Jiang, Rujia Shen, Boran Wang +1
Type 1 diabetes mellitus (T1D) is characterized by insulin deficiency and blood glucose (BG) control issues. The state-of-the-art solution for continuous BG control is reinforcemen…
Causal Coupled Mechanisms: A Control Method with Cooperation and Competition for Complex System
Xuehui Yu, Jingchi Jiang, Xinmiao Yu +2
Complex systems are ubiquitous in the real world and tend to have complicated and poorly understood dynamics. For their control issues, the challenge is to guarantee accuracy, robu…