Representation Learning via Invariant Causal MechanismsJovana MitrovicBrian McWilliamsJacob C WalkerLars Holger BuesingCharles BlundellInternational Conference on Learning Representations
ReLIC, or Representation Learning via Invariant Causal Mechanisms, is a self-supervised learning objective that enforces invariant prediction of proxy targets across augmentations through an invariance regularizer which yields improved generalization guarantees. We can write the objective as:$$\underset{X}...
That being said, CIGA is definitely not the ultimate solution and it intrinsically has many limitations. Nevertheless, we hope the causal analysis and the inspired solution in CIGA could serve as an initial step towards more reliable graph learning algorithms that are able to generalize various OOD...
Yang, M., Liu, F., Chen, Z., Shen, X., Hao, J., & Wang, J. (2021). Causalvae: Disentangled representation learning via neural structural causal models. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition(pp. 9593–9602). Zeng, Z., Wang, Z., Wang,...
5 Generalization Bounds and Links to Causal Inference 虽然DBC支持无需像素重建的表征学习,但它留下了一个问题,即生成的表征到底有多好。在本节中,我们将进行理论分析,以限制通过DBC学习的表征训练的价值函数的次优性。 MDP动力学与因果推断和因果图有很强的联系,因果图是有向无环图(Jonsson & Barto,2006;Sch...
By leveraging data from multiple environments, we propose Invariant Causal Imitation Learning (ICIL), a novel technique in which we learn a feature representation that is invariant across domains, on the basis of which we learn an imitation policy that matches expert behavior. To cope with ...
Concepts of causal representation learning (Schlkopf et al., 2021) (Section 2.5.3) can help when defining and becoming robust to domain shifts when there are data biases (Arjovsky et al., 2019; Krueger et al., 2021). Recent work (Wang et al., 2021d) disentangles group-invariant ...
具体的,causal error是通过计算 \hat{M}_{1\rightarrow y},W_{1\rightarrow y} 的均方误差来衡量,non-causal error是通过计算 \hat{M}_{y\rightarrow 2},W_{y\rightarrow 2} 的均方误差来,y轴是log(error)。我们希望模型的non-casual error接近0。 得到的结论如下:1. IRMv1在non-casual和causal ...
Causalrepresentationlearning. Neuralnetworksmaylearnundesirable“shortcuts”fortheirtasks – e.g., classifying images based on the texture of the background. To mitigate thisissue, various schemes have been proposed to force the network to use causally relevantfactors in its decision (e.g., Veitch ...
Causal-IR: Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective Xin Li, Bingchen Li, Xin Jin, Cuiling Lan, Zhibo Chen University of Science and Technology of China (USTC), Microsoft Research Asia (MSRA), Eastern Institute of Technology (EIT) New!!! |...