Using AI/ML to do moar Sciencing!

The world we live in is causal, yet many Artificial Intellgience systems and most Machine Learning systems ignore this reality for the sake of convenience. There is a growing interest in making progress in this important concept, and this space will highlight our research in that area.

Our Papers on Causality

  1. GCRL
    Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
    In International Conference on Machine Learning (ICML). Honolulu, Hawaii, USA. Jul, 2023.
  2. Cyclic causal models with discrete variables: Markov chain equilibrium semantics and sample ordering
    David Poole, and Mark Crowley
    In International Joint Conference on Artificial Intelligence (IJCAI). Beijing, China. 2013.