Penalised/regularised regression

1 Connection to Bayesian priors

Famously, a penalty can have an interpretation as a Bayesian prior on the solution space. It is a fun exercise for example to “rediscover” the lasso regression as a typical linear regression but with the plus prize for the coefficients. In that case the maximum a posteriori estimate given those bays rise and the lasso solution coincide. If you want to know the full posterior you have to do a lot more work. But the connection is suggestive nonetheless.

A related and useful connection the interpretation of covariance kernels as prize producing smoothness in solutions. A very elegant introduction to these is given in Miller, Glennie, and Seaton (2020).

2 As shrinkage

TBD. James and Stein (1961). beautiful explainer video.

3 Adaptive regularization

What should we regularize to attain specific kinds of solutions?

Here’s one thing I saw recently:

Venkat Chandrasekaran, Learning Semidefinite Regularizers via Matrix Factorization

Abstract: Regularization techniques are widely employed in the solution of inverse problems in data analysis and scientific computing due to their effectiveness in addressing difficulties due to ill-posedness. In their most common manifestation, these methods take the form of penalty functions added to the objective in optimization-based approaches for solving inverse problems. The purpose of the penalty function is to induce a desired structure in the solution, and these functions are specified based on prior domain-specific expertise. We consider the problem of learning suitable regularization functions from data in settings in which prior domain knowledge is not directly available. Previous work under the title of ‘dictionary learning’ or ‘sparse coding’ may be viewed as learning a polyhedral regularizer from data. We describe generalizations of these methods to learn semidefinite regularizers by computing structured factorizations of data matrices. Our algorithmic approach for computing these factorizations combines recent techniques for rank minimization problems along with operator analogs of Sinkhorn scaling. The regularizers obtained using our framework can be employed effectively in semidefinite programming relaxations for solving inverse problems. (Joint work with Yong Sheng Soh)

4 References