INFOCOM
2012
IEEE
12 years 3 months ago
2012
IEEE
—We study two convex optimization problems in a multi-class M/G/1 queue with adjustable service rates: minimizing convex functions of the average delay vector, and minimizing ave...
ICCV
2011
IEEE
13 years 22 days ago
2011
IEEE
Our objective is transfer training of a discriminatively trained object category detector, in order to reduce the number of training images required. To this end we propose three ...
ICASSP
2011
IEEE
13 years 4 months ago
2011
IEEE
This paper addresses the optimization of upper and lower bounds on the capacity of the multiple-input multiple-output (MIMO) relay channel. In particular, we show that evaluating ...
CDC
2010
IEEE
13 years 7 months ago
2010
IEEE
In this paper we study the constrained consensus problem, i.e. the problem of reaching a common point from the estimates generated by multiple agents that are constrained to lie in...
EMNLP
2009
13 years 10 months ago
2009
The recently introduced online confidence-weighted (CW) learning algorithm for binary classification performs well on many binary NLP tasks. However, for multi-class problems CW l...
FOCM
2010
13 years 11 months ago
2010
We show how to approximate the feasible region of structured convex optimization problems by a family of convex sets with explicitly given and efficient (if the accuracy of the ap...
NIPS
2001
14 years 2 months ago
2001
Greedy approximation algorithms have been frequently used to obtain sparse solutions to learning problems. In this paper, we present a general greedy algorithm for solving a class...
AAAI
2006
14 years 2 months ago
2006
L1 regularized logistic regression is now a workhorse of machine learning: it is widely used for many classification problems, particularly ones with many features. L1 regularized...
NIPS
2008
14 years 2 months ago
2008
We consider robust least-squares regression with feature-wise disturbance. We show that this formulation leads to tractable convex optimization problems, and we exhibit a particul...
ICML
2006
IEEE
15 years 1 months ago
2006
IEEE
An intuitive approach to utilizing unlabeled data in kernel-based classification algorithms is to simply treat unknown labels as additional optimization variables. For marginbased...