Temporally Disentangled Representation Learning
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时间解耦的表征学习
- 研究问题
- 当潜在变量之间存在非参数、非线性的时间因果关系时，能否从观测序列的非线性混合中识别潜在变量及其关系？
- 方法与思路
- 作者提出 TDRL 框架，利用时间滞后关系及分布变化学习潜在表征，并区分转移分布、潜在因果关系和观测机制的变化。
- 材料陈述的贡献
- 摘要称建立了固定时间因果影响下的非参数潜在过程识别理论，并通过实验展示关系恢复及相对所选基准的性能改善。
- 阅读关注点
- 应核对识别所需的时间结构、分布及观测机制条件，以及固定关系与时变关系下的结论如何区分，实验如何衡量恢复准确性。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未说明完整识别条件，也不能证明适用于任意分布变化。
原文摘要
Recently in the field of unsupervised representation learning, strong identifiability results for disentanglement of causally-related latent variables have been established by exploiting certain side information, such as class labels, in addition to independence. However, most existing work is constrained by functional form assumptions such as independent sources or further with linear transitions, and distribution assumptions such as stationary, exponential family distribution. It is unknown whether the underlying latent variables and their causal relations are identifiable if they have arbitrary, nonparametric causal influences in between. In this work, we establish the identifiability theories of nonparametric latent causal processes from their nonlinear mixtures under fixed temporal causal influences and analyze how distribution changes can further benefit the disentanglement. We propose \textbf{\texttt{TDRL}}, a principled framework to recover time-delayed latent causal variables and identify their relations from measured sequential data under stationary environments and under different distribution shifts. Specifically, the framework can factorize unknown distribution shifts into transition distribution changes under fixed and time-varying latent causal relations, and under observation changes in observation. Through experiments, we show that time-delayed latent causal influences are reliably identified and that our approach considerably outperforms existing baselines that do not correctly exploit this modular representation of changes. Our code is available at: \url{https://github.com/weirayao/tdrl}.