MIT Libraries homeDSpace@MIT

A crucial challenge in robotics is achieving reliable results in spite of sensing and control uncertainty. A prominent strategy for dealing with uncertainty is to construct a feedback policy, where actions are chosen as a ...

In this work we are interested in the problems of supervised learning and variable selection when the input-output dependence is described by a nonlinear function depending on a few variables. Our goal is to consider a ...

Within the framework of statistical learning theory we analyze in detail the so-called elastic-net regularization scheme proposed by Zou and Hastie ["Regularization and variable selection via the elastic net" J. R. Stat. ...

We present AvatarSAT, a SAT solver that uses machine-learning classifiers to automatically tune the heuristics of an off-the-shelf SAT solver on a per-instance basis. The classifiers use features of both the input and ...

We study multi-category classification in the framework of computational learning theory. We show how a relaxation approach, which is commonly used in binary classification, can be generalized to the multi-class setting. ...

Large data sets are often modeled as being noisy samples from probability distributions in R^D, with D large. It has been noticed that oftentimes the support M of these probability distributions seems to be well-approximated ...

We consider regularized least-squares (RLS) with a Gaussian kernel. Weprove that if we let the Gaussian bandwidth $\sigma \rightarrow\infty$ while letting the regularization parameter $\lambda\rightarrow 0$, the RLS solution ...

Clustering methods optimize the partitioning of data points with respect to an internal metric, such as likelihood, in order to approximate the goodness of clustering. However, this internal metric does not necessarily ...

In cloud-computing systems, network-bandwidth guarantees have been shown to improve predictability of application performance and cost. Most previous work on cloud-bandwidth guarantees has assumed that cloud tenants know ...