(I’m channeling for Jean-Yves Audibert here, with some minor tweaking for clarity.)

Since Nemirovski’s Saint Flour lecture notes, numerous researchers have studied the following problem in least squares regression: predict as well as

(MS) the best of d given functions (like in prediction with expert advice; model = finite set of d functions)

(C) the best convex combination of these functions (i.e., model = convex hull of the d functions)

(L) the best linear combination of these functions (i.e., model = linear span of the d functions)

It is now well known (see, e.g., Sacha Tsybakov’s COLT’03 paper) that these tasks can be achieved since there exist estimators having an excess risk of order (log d)/n for (MS), min( sqrt((log d)/n), d/n ) for (C) and d/n for (L), where n is the training set size. Here, “risk” is amount of extra loss per example which may be suffered due to the choice of random sample.

The practical use of these results seems rather limited to trivial statements like: do not use the OLS estimator when the dimension d of the input vector is larger than n (here the d functions are the projections on each of the d components). Nevertheless, it provides a rather easy way to prove that there exists a learning algorithm having an excess risk of order s (log d)/n, with respect to the best linear combination of s of the d functions (s-sparse linear model). Indeed, it suffices to consider the algorithm which

- cuts the training set into two parts, say of equal size for simplicity,
- uses the first part to train linear estimators corresponding to every possible subset of s features. Here you can use your favorite linear estimator (the empirical risk minimizer on a compact set or robust but more involved ones are possible rather than the OLS), as long as it solves (L) with minimal excess risk.
- uses the second part to predict as well as the “d choose s” linear estimators built on the first part. Here you choose your favorite aggregate solving (MS). The one I prefer is described in p.5 of my NIPS’07 paper, but you might prefer the progressive mixture rule or the algorithm of Guillaume LecuĂ© and Shahar Mendelson. Note that empirical risk minimization and cross-validation completely fail for this task with excess risk of order sqrt((log d)/n) instead of (log d)/n.

It is an easy exercise to combine the different excess risk bounds and obtain that the above procedure achieves an excess risk of s (log d)/n. The nice thing compared to works on Lasso, Dantzig selectors and their variants is that you do not need all these assumptions saying that your features should be “not too much” correlated. Naturally, the important limitation of the above procedure, which is often encountered when using classical model selection approach, is its computational intractability. So this leaves open the following fundamental problem:

is it possible to design a computationally efficient algorithm with the s (log d)/n guarantee without assuming low correlation between the explanatory variables?