Is there any ongoing research on known patterns ML algorithms can not mine? I have been doing ML for sometimes and have explored and implemented Deep Learning as for some projects. ML algorithms assume a certain pattern (Geometric/Bayesian/Distribution) in the data .This can usually be referred as Hypothesis Space in the literature, then we design a solution to best fit that hypothesis to our data. 

I wanted to know are there some patterns (Hypothesis spaces), which do not yet have a reliable algorithm to be mined? 

Like Decision Trees used the Information Theory to assume that Information Gain from a variable can help us better classify the data. This was a new approach than SVM which assumes a geometrical space to where a plane can classify the data. I hope there must me some new theories which are being worked on.

Let me know if I can improve this question.
The closest I can think of is research into "adversarial" transformations of data (usually images) that cause classifiers (usually CNNs) to fail. Here's a blog post which discusses a bit about how these transformations are constructed, some approaches that have been tried to address them, and what some of the consequences of their existence might be.

You might also be interested to read about the No Free Lunch Theorem.