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127
Voted
NIPS
2007
15 years 4 months ago
2007
Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
138
Voted
UAI
1998
15 years 4 months ago
1998
tigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current models that combine both primitive actions and macro-...
127
Voted
UAI
2000
15 years 4 months ago
2000
In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different c...
138
Voted
ICML
2008
IEEE
16 years 3 months ago
2008
IEEE
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of th...
130
Voted
PODS
2004
ACM
16 years 2 months ago
2004
ACM
We study the problem of answering queries over sources with limited access patterns. Given a first-order query Q, the problem is to decide whether there is an equivalent query whi...