We present a new algorithm for minimizing a convex loss-function subject to regularization. Our framework applies to numerous problems in machine learning and statistics; notably,...
Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is ...
Niranjan Srinivas, Andreas Krause, Sham Kakade, Ma...
A sparse representation of Support Vector Machines (SVMs) with respect to input features is desirable for many applications. In this paper, by introducing a 0-1 control variable t...
Decoupling capacitor (decap) placement has been widely adopted as an effective way to suppress dynamic power supply noise. Traditional decap budgeting algorithms usually explore t...
Recent convergence between low-cost technology, artform and political discourse presents a new design space for enabling public participation and expression. We explore non-expert...