Thermal gradients across the die are becoming increasingly prominent as we scale further down into the sub-nanometer regime. While temperature was never a primary concern, its non...
We study the convergence properties of an alternating proximal minimization algorithm for nonconvex structured functions of the type: L(x, y) = f(x)+Q(x, y)+g(y), where f : Rn → ...
Scoring rules for eliciting expert predictions of random variables are usually developed assuming that experts derive utility only from the quality of their predictions (e.g., sco...
A fundamental problem in distributed computation is the distributed evaluation of functions. The goal is to determine the value of a function over a set of distributed inputs, in ...
Most of the existing active learning algorithms are based on the realizability assumption: The learner’s hypothesis class is assumed to contain a target function that perfectly c...