Scalable Causal Domain Adaptation
One of the most critical problems in transfer learning is the task of domain adaptation, where the goal is to apply an algorithm trained in one or more source domains to a different (but related) target domain. This paper deals with domain adaptation in the presence of covariate shift while invariances exist across domains. One of the main limitations of existing causal inference methods for solving this problem is scalability. To overcome this difficulty, we propose SCTL, an algorithm that avoids an exhaustive search and identifies invariant causal features across source and target domains based on Markov blanket discovery. SCTL does not require having prior knowledge of the causal structure, the type of interventions, or the intervention targets. There is an intrinsic locality associated with SCTL that makes it practically scalable and robust because local causal discovery increases the power of computational independence tests and makes the task of domain adaptation computationally tractable. We show the scalability and robustness of SCTL for domain adaptation using synthetic and real data sets in low-dimensional and high-dimensional settings.
Code (1)
Tasks
Causal DiscoveryCausal InferenceDomain AdaptationTransfer LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Causality Meets Locality: Provably Generalizable and Scalable Policy Learning for Networked Systems
Large-scale networked systems, such as traffic, power, and wireless grids, challenge reinforcement-learning agents with both scale and environment shifts. To address these challenges, we propose GSAC (Generalizable and S…
Representation LearningDomain GeneralizationDomain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization
Monitoring organic matter is pivotal for maintaining soil health and can help inform sustainable soil management practices. While sensor-based soil information offers higher-fidelity and reliable insights into organic ma…
Contrastive LearningDomain AdaptationManagementCausal Evaluation of Language Models
Causal reasoning is viewed as crucial for achieving human-level machine intelligence. Recent advances in language models have expanded the horizons of artificial intelligence across various domains, sparking inquiries in…
Causal DiscoveryCausal InferenceLanguage ModellingDeep causal representation learning for unsupervised domain adaptation
Studies show that the representations learned by deep neural networks can be transferred to similar prediction tasks in other domains for which we do not have enough labeled data. However, as we transition to higher laye…
Domain AdaptationRepresentation LearningUnsupervised Domain AdaptationLearning causal representations for robust domain adaptation
Domain adaptation solves the learning problem in a target domain by leveraging the knowledge in a relevant source domain. While remarkable advances have been made, almost all existing domain adaptation methods heavily re…
Domain Adaptation