EOR
2010
13 years 5 months ago
2010
Abstract: Probabilistically constrained problems, in which the random variables are finitely distributed, are nonconvex in general and hard to solve. The p-efficiency concept has b...
CPC
2010
13 years 5 months ago
2010
We consider the minimum-weight path between any pair of nodes of the n-vertex complete graph in which the weights of the edges are i.i.d. exponentially distributed random variable...
AI
2008
Springer
13 years 5 months ago
2008
Springer
Graphs provide an excellent framework for interrogating symmetric models of measurement random variables and discovering their implied conditional independence structure. However,...
ECCV
2010
Springer
13 years 5 months ago
2010
Springer
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
ICML
2010
IEEE
13 years 6 months ago
2010
IEEE
We consider the general, widely applicable problem of selecting from n real-valued random variables a subset of size m of those with the highest means, based on as few samples as ...
AIPS
2009
13 years 6 months ago
2009
We define the robustness of a sequential plan as the probability that it will execute successfully despite uncertainty in the execution environment. We consider a rich notion of u...
NIPS
2003
13 years 6 months ago
2003
The detection and pose estimation of people in images and video is made challenging by the variability of human appearance, the complexity of natural scenes, and the high dimensio...
NIPS
2004
13 years 6 months ago
2004
Directed graphical models with one layer of observed random variables and one or more layers of hidden random variables have been the dominant modelling paradigm in many research ...
AAAI
2006
13 years 6 months ago
2006
It is often convenient to represent probabilistic models in a first-order fashion, using logical atoms such as
APPROX
2010
Springer
13 years 6 months ago
2010
Springer
We give a simple combinatorial proof of the Chernoff-Hoeffding concentration bound [Che52, Hoe63], which says that the sum of independent {0, 1}-valued random variables is highly ...