Sciweavers

UAI
2004
13 years 6 months ago
The Author-Topic Model for Authors and Documents
Michal Rosen-Zvi, Thomas L. Griffiths, Mark Steyve...
UAI
2004
13 years 6 months ago
Variational Chernoff Bounds for Graphical Models
Recent research has made significant progress on the problem of bounding log partition functions for exponential family graphical models. Such bounds have associated dual paramete...
Pradeep D. Ravikumar, John D. Lafferty
UAI
2004
13 years 6 months ago
Robustness of Causal Claims
A causal claim is any assertion that invokes causal relationships between variables, for example, that a drug has a certain e ect on preventing a disease. Causal claims are establ...
Judea Pearl
UAI
2004
13 years 6 months ago
Robust Probabilistic Inference in Distributed Systems
Probabilistic inference problems arise naturally in distributed systems such as sensor networks and teams of mobile robots. Inference algorithms that use message passing are a nat...
Mark A. Paskin, Carlos Guestrin
UAI
2004
13 years 6 months ago
On Modeling Profiles Instead of Values
We consider the problem of estimating the distribution underlying an observed sample of data. Instead of maximum likelihood, which maximizes the probability of the observed values...
Alon Orlitsky, Narayana P. Santhanam, Krishnamurth...
UAI
2004
13 years 6 months ago
MOB-ESP and other Improvements in Probability Estimation
A key prerequisite to optimal reasoning under uncertainty in intelligent systems is to start with good class probability estimates. This paper improves on the current best probabi...
Rodney Nielsen
UAI
2004
13 years 6 months ago
PAC-learning Bounded Tree-width Graphical Models
We show that the class of strongly connected graphical models with treewidth at most k can be properly efficiently PAC-learnt with respect to the Kullback-Leibler Divergence. Prev...
Mukund Narasimhan, Jeff A. Bilmes
UAI
2004
13 years 6 months ago
"Ideal Parent" Structure Learning for Continuous Variable Networks
In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive proced...
Iftach Nachman, Gal Elidan, Nir Friedman
UAI
2004
13 years 6 months ago
Bayesian Learning in Undirected Graphical Models: Approximate MCMC Algorithms
Bayesian learning in undirected graphical models--computing posterior distributions over parameters and predictive quantities-is exceptionally difficult. We conjecture that for ge...
Iain Murray, Zoubin Ghahramani
UAI
2004
13 years 6 months ago
Convolutional Factor Graphs as Probabilistic Models
Based on a recent development in the area of error control coding, we introduce the notion of convolutional factor graphs (CFGs) as a new class of probabilistic graphical models. ...
Yongyi Mao, Frank R. Kschischang, Brendan J. Frey