We address the problem of finding the most likely assignment or MAP estimation in a Markov random field. We analyze the linear programming formulation of MAP through the lens of...
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Assuming the existence of one-way functions, we show that there is no polynomial-time, differentially private algorithm A that takes a database D ({0, 1}d )n and outputs a "...
Optimization algorithms for large margin multiclass recognizers are often too costly to handle ambitious problems with structured outputs and exponential numbers of classes. Optim...
This paper proposes two new methods for optimizing objectives and constraints. The GP approach is very general and hardware resources in finite wordlength implementation of it allo...