Shayan Shirahmad Gale Bagi

Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting

Conventional supervised learning methods typically assume i.i.d samples and are found to be sensitive to out-of-distribution (OOD) data. We propose Generative Causal Representation Learning (GCRL) which leverages causality to facilitate knowledge transfer under distribution shifts. While we evaluate…