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NIPS
1998
13 years 9 months ago
Learning from Dyadic Data
Dyadic data refers to a domain with two nite sets of objects in which observations are made for dyads, i.e., pairs with one element from either set. This type of data arises natur...
Thomas Hofmann, Jan Puzicha, Michael I. Jordan
CORR
2008
Springer
107views Education» more  CORR 2008»
13 years 7 months ago
Metric Structures and Probabilistic Computation
Abstract. Continuous first-order logic is used to apply model-theoretic analysis to analytic structures (e.g. Hilbert spaces, Banach spaces, probability spaces, etc.). Classical co...
Wesley Calvert
JMLR
2011
142views more  JMLR 2011»
13 years 2 months ago
Causal Search in Structural Vector Autoregressive Models
This paper reviews a class of methods to perform causal inference in the framework of a structural vector autoregressive model. We consider three different settings. In the first ...
Alessio Moneta, Nadine Chlass, Doris Entner, Patri...
PODS
2007
ACM
196views Database» more  PODS 2007»
14 years 7 months ago
On the complexity of managing probabilistic XML data
In [3], we introduced a framework for querying and updating probabilistic information over unordered labeled trees, the probabilistic tree model. The data model is based on trees ...
Pierre Senellart, Serge Abiteboul
KDD
2003
ACM
269views Data Mining» more  KDD 2003»
14 years 8 months ago
Maximizing the spread of influence through a social network
Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technolog...
David Kempe, Jon M. Kleinberg, Éva Tardos