2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024
On the Identification of Temporally Causal Representation with Instantaneous Dependence
Zijian Li, Yifan Shen, Kaitao Zheng +5
Temporally causal representation learning aims to identify the latent causal process from time series observations, but most methods require the assumption that the latent causal p…
cs.LG2023★ 2 cited
Learning World Models with Identifiable Factorization
Yu-Ren Liu, Biwei Huang, Zhengmao Zhu +4
Extracting a stable and compact representation of the environment is crucial for efficient reinforcement learning in high-dimensional, noisy, and non-stationary environments. Diffe…
cs.LG2023
Beware of Instantaneous Dependence in Reinforcement Learning
Zhengmao Zhu, Yuren Liu, Honglong Tian +2
Playing an important role in Model-Based Reinforcement Learning (MBRL), environment models aim to predict future states based on the past. Existing works usually ignore instantaneo…