1. Introduction In the past decade, machine learning has been playing an increasingly important role in the revolution of artificial intelligence (AI). As the products and services driven by machine learning technology are becoming widespread in our daily life, in particular in those life-critical applications (e.g., self-driving, medicine, healthcare, etc.), the demand for artificial intelligence … Continue reading The Agnostic Hypothesis: A Unifying View of Machine Learning
This post is motivated by my recent read of Invariant Risk Minimization (IRM) by Arjovsky et al. (2019). When it comes to the identically and independently distributed (iid) assumption in its concluding dialogue, the image (e.g., MNIST) classification example triggered an interesting discussion on the relationship between causal/anticausal learning and supervised/unsupervised learning. Simply put, the … Continue reading Is Image Classification a Causal Problem?
Both reinforcement learning (RL)  and causal inference  are indispensable part of machine learning and each plays an essential role in artificial intelligence. What originally motivated me to integrate both is the recent development of machine learning in healthcare and medicine. In retrospect, human beings, since their birth, have been inevitably accompanied by diseases … Continue reading Introduction to Causal RL