4 papers · 1 filter
A Dialogue between Causal and Traditional Representation Learning: Toward Mutual Benefits in a Unified Formulation
Yan Li, Yuewen Sun, Shaoan Xie +4
Causal representation learning (CRL) and traditional representation learning have largely developed along different trajectories. Traditional representation learning has been drive…
A General Representation-Based Approach to Multi-Source Domain Adaptation
Ignavier Ng, Yan Li, Zijian Li +3
A central problem in unsupervised domain adaptation is determining what to transfer from labeled source domains to an unlabeled target domain. To handle high-dimensional observatio…
Should Bias be Eliminated? A General Framework to Use Bias for OOD Generalization
Yan Li, Yunlong Deng, Zijian Li +4
Most approaches to out-of-distribution (OOD) generalization learn domain-invariant representations by discarding contextual bias. In this paper, we raise a critical question: Shoul…
CausalVerse: Benchmarking Causal Representation Learning with Configurable High-Fidelity Simulations
Guangyi Chen, Yunlong Deng, Peiyuan Zhu +4
Causal Representation Learning (CRL) aims to uncover the data-generating process and identify the underlying causal variables and relations, whose evaluation remains inherently cha…