INFOCOM
2012
IEEE
11 years 7 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
12 years 4 months 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
12 years 8 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
12 years 11 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 2 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 3 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
13 years 5 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
13 years 6 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
13 years 6 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
14 years 5 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...