We perform a smoothed analysis of Renegar’s condition number for linear programming by analyzing the distribution of the distance to ill-posedness of a linear program subject to...
Active learning is well-suited to many problems in natural language processing, where unlabeled data may be abundant but annotation is slow and expensive. This paper aims to shed ...
In this paper we model relational random variables on the edges of a network using Gaussian processes (GPs). We describe appropriate GP priors, i.e., covariance functions, for dir...
We describe a system as a set of communicating concurrent programs. Quasi-static scheduling compiles the concurrent programs into a sequential one. It uses a Petri net as an inter...
Cong Liu, Alex Kondratyev, Yosinori Watanabe, Albe...
The kernel Perceptron is an appealing online learning algorithm that has a drawback: whenever it makes an error it must increase its support set, which slows training and testing ...