5 papers
Block Disentanglement in CRL: Bridging Identifiability and Visual State Estimation
Emre Acartürk, Pranamya Kulkarni, Puranjay Datta +3
Causal representation learning (CRL) is the process of recovering causally-related latent variables from high-dimensional observations. As a label-free inference method, CRL is par…
ROPES: Robotic Pose Estimation via Score-Based Causal Representation Learning
Pranamya Kulkarni, Puranjay Datta, Burak Varıcı +3
Causal representation learning (CRL) has emerged as a powerful unsupervised framework that (i) disentangles the latent generative factors underlying high-dimensional data, and (ii)…
Linear Causal Representation Learning from Unknown Multi-node Interventions
Burak Varıcı, Emre Acartürk, Karthikeyan Shanmugam +1
Despite the multifaceted recent advances in interventional causal representation learning (CRL), they primarily focus on the stylized assumption of single-node interventions. This…
Score-based Causal Representation Learning: Linear and General Transformations
Burak Varıcı, Emre Acartürk, Karthikeyan Shanmugam +2
This paper addresses intervention-based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown transformation that maps the latent va…
Score-based Causal Representation Learning with Interventions
Burak Varici, Emre Acarturk, Karthikeyan Shanmugam +2
This paper studies the causal representation learning problem when the latent causal variables are observed indirectly through an unknown linear transformation. The objectives are:…