Causal reinforcement learning: A survey
Reinforcement learning is an essential paradigm for solving sequential decision problems
under uncertainty. Despite many remarkable achievements in recent decades, applying …
under uncertainty. Despite many remarkable achievements in recent decades, applying …
Nonparametric identifiability of causal representations from unknown interventions
We study causal representation learning, the task of inferring latent causal variables and
their causal relations from high-dimensional functions (“mixtures”) of the variables. Prior …
their causal relations from high-dimensional functions (“mixtures”) of the variables. Prior …
Learning linear causal representations from interventions under general nonlinear mixing
We study the problem of learning causal representations from unknown, latent interventions
in a general setting, where the latent distribution is Gaussian but the mixing function is …
in a general setting, where the latent distribution is Gaussian but the mixing function is …
Causal component analysis
Abstract Independent Component Analysis (ICA) aims to recover independent latent
variables from observed mixtures thereof. Causal Representation Learning (CRL) aims …
variables from observed mixtures thereof. Causal Representation Learning (CRL) aims …
Causal deep learning: encouraging impact on real-world problems through causality
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Deep causal factorization network: A novel domain generalization method for cross-machine bearing fault diagnosis
S Jia, Y Li, X Wang, D Sun, Z Deng - Mechanical Systems and Signal …, 2023 - Elsevier
Abstract Domain generalization (DG) has attracted much attention in bearing fault diagnosis
since it can generalize the prior diagnostic knowledge to invisible working conditions …
since it can generalize the prior diagnostic knowledge to invisible working conditions …
Causal deep learning
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Nonparametric partial disentanglement via mechanism sparsity: Sparse actions, interventions and sparse temporal dependencies
This work introduces a novel principle for disentanglement we call mechanism sparsity
regularization, which applies when the latent factors of interest depend sparsely on …
regularization, which applies when the latent factors of interest depend sparsely on …
From identifiable causal representations to controllable counterfactual generation: A survey on causal generative modeling
Deep generative models have shown tremendous success in data density estimation and
data generation from finite samples. While these models have shown impressive …
data generation from finite samples. While these models have shown impressive …
Jacobian-based causal discovery with nonlinear ICA
Today's methods for uncovering causal relationships from observational data either
constrain functional assignments (linearity/additive noise assumptions) or the data …
constrain functional assignments (linearity/additive noise assumptions) or the data …