Learnability of constraint satisfaction problems CSPs? This may sound more like a soft question but I am struggling to find an answer for it. 

While the learnability of Bayesian Networks and other graphical models are well detailed in the literature of machine learning and learning theory, there is almost nothing on learning CSPs. Given that CSP is a well known constraint formalism, I found this very weird. Am I missing something here?
You are probably looking for this paper:


Víctor Dalmau and Peter Jeavons, Learnability of quantified formulas, TCS 306 485–511, 2003.  doi:10.1016/S0304-3975(03)00342-6


In short, the learning complexity of a family of quantified formulas over a finite domain of values is determined by its clone of polymorphisms.  This includes CSPs as a special case of more general quantified formulas (since a CSP instance is just an existentially quantified conjunctive formula).

For the Boolean case and also for two broad classes of non-Boolean formulas, there is a dichotomy between polynomially learnable (via the Generating Set algorithm, if the clone contains a "nice" operation) and not, subject to the hypothesis that there exists a public key cryptosystem secure against chosen ciphertext attacks.