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» Learning Causal Models of Relational Domains
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SOCIALCOM
2010
13 years 7 months ago
A Methodology for Integrating Network Theory and Topic Modeling and its Application to Innovation Diffusion
Text data pertaining to socio-technical networks often are analyzed separately from relational data, or are reduced to the fact and strength of the flow of information between node...
Jana Diesner, Kathleen M. Carley
AAAI
2011
12 years 9 months ago
Using Semantic Cues to Learn Syntax
We present a method for dependency grammar induction that utilizes sparse annotations of semantic relations. This induction set-up is attractive because such annotations provide u...
Tahira Naseem, Regina Barzilay
ICML
2006
IEEE
14 years 10 months ago
Cost-sensitive learning with conditional Markov networks
There has been a recent, growing interest in classification and link prediction in structured domains. Methods such as conditional random fields and relational Markov networks sup...
Prithviraj Sen, Lise Getoor
AAMAS
2007
Springer
14 years 4 months ago
Bifurcation Analysis of Reinforcement Learning Agents in the Selten's Horse Game
Abstract. The application of reinforcement learning algorithms to multiagent domains may cause complex non-convergent dynamics. The replicator dynamics, commonly used in evolutiona...
Alessandro Lazaric, Jose Enrique Munoz de Cote, Fa...
ICML
2005
IEEE
14 years 10 months ago
Reducing overfitting in process model induction
In this paper, we review the paradigm of inductive process modeling, which uses background knowledge about possible component processes to construct quantitative models of dynamic...
Will Bridewell, Narges Bani Asadi, Pat Langley, Lj...