Simple Expression Related to Mutual Information One way to define the mutual information is
$I(X;Y) = H(X) - H(X|Y)$
I have found it useful to look the related quantity
$?(X;Y=y) = H(X) - H(X|Y=y)$
That is, we look at how much the entropy of $X$ is decreased given a particular outcome $y$ for $Y$.
It is not hard to see that the mutual information is regained on expectation on $Y$, so that we have
$E_Y[?(X;Y=y)] \\
= \sum_y p(y)(H(X) - H(X|Y=y)) \\
= H(X) - \sum_y p(y)H(X|Y=y) \\
= H(X) - H(X|Y) \\
= I(X;Y)$
My question: does my $?(X;Y=y)$ function have a name? Or a standardized notation?
Note it's not the same as pointwise mutual information: rather, I think this would be the expectation of pointwise mutual information, but only on $X$ (rather than both variables). So it's "in between" the regular mutual information and the pointwise version.
 A: This measure was probably first proposed and analyzed in 

N Blachman. "The amount of information that $y$ gives about $X$."  IEEE Transactions on Information Theory, (1968): 27-31.

More recently, it has attracted attention in the neuroscience community.  In particular, see the definition of $I_2$ in Eq. 6 in

MR DeWeese and M Meister. "How to measure the information gained from one symbol." Network: Computation in Neural Systems (1999): 325-340.

and various articles that cite DeWeese and Meister. In most of these citing articles, $I_2$ has been called "specific information"; however, some care is required, because specific information can also (more rarely) refer to $I_1 := D(p(X|y)|| p(X))$ (note that mutual information can be written as an expectation of either $I_1$ or $I_2$; $I_1$ is sometimes called "specific surprise").  
$I_2$ also goes by various other names, including "predictability" (R Bramon et al. "Multimodal data fusion based on mutual information." IEEE Transactions on Visualization and Computer Graphics, (2012): 1574-1587.), and the "i-measure" (P Smyth and RM Goodman. "An information theoretic approach to rule induction from databases." IEEE transactions on Knowledge and data engineering, (1992): 301-316, Eq. 2).
