Learning from All Types of Experiences: A Unifying Machine Learning Perspective

Contemporary Machine Learning and AI research has resulted in thousands of models (e.g., numerous deep networks, graphical models), learning paradigms (e.g., supervised, unsupervised, active, reinforcement, adversarial learning), optimization techniques (e.g., all kinds of optimization or stochastic sampling algorithms), not mentioning countless approximation heuristics, tuning tricks, and black-box oracles, plus combinations of all above. While pushing the field forward rapidly, these results also contributed to making ML/AI more like an alchemist's crafting workshop rather than a modern chemist's periodic table. It not only makes mastering existing ML techniques extremely difficult, but also makes standardized, reusable, repeatable, reliable, and explainable practice and further development of ML/AI products extremely costly, if possible at all.