Active Learning of Multi-Index Function Models

We propose a randomized, active sampling scheme for estimating such functions with uniform approximation guarantees. Our theoretical developments leverage recent techniques from low rank matrix recovery, which enables us to derive an estimator of the function f along with sample complexity bounds. We also characterize the noise robustness of the scheme, and provide empirical evidence that the highdimensional scaling of our sample complexity bounds are quite accurate.