On Practical Challenges of Scaling Causal Representation Learning

Abstract

I was invited to give a talk on our recent works in causal representation learning (CRL), where we have the goal of making CRL more practical and scale towards realistic environments. I covered our experience in optimizing CRL setups in our works CITRIS, iCITRIS and BISCUIT. Finally, I discussed future visions for CRL in practice.