Statistical inference for high-dimensional regression models is a challenging problem that has received extensive attention recently. Existing methods focus on inference for finite-dimensional components of the model parameters. Construction of parameter estimators and establishing asymptotic inference are specific to each specific model. In this talk, I will briefly review some of our recent work to construct a general framework to conduct statistical inference for high-dimensional problems.