We present a new method for regularized convex optimization and analyze it under both online and stochastic optimization settings. In addition to unifying previously known firstor...
John Duchi, Shai Shalev-Shwartz, Yoram Singer, Amb...
Many semi-supervised learning algorithms only
deal with binary classification. Their extension to the
multi-class problem is usually obtained by repeatedly
solving a set of bina...
We propose a functional mixture model for simultaneous clustering and alignment of sets of curves measured on a discrete time grid. The model is specifically tailored to gene exp...
Darya Chudova, Christopher E. Hart, Eric Mjolsness...
—We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selectio...
Many time-series experiments seek to estimate some signal as a continuous function of time. In this paper, we address the sampling problem for such experiments: determining which ...
Rohit Singh, Nathan Palmer, David K. Gifford, Bonn...