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ICML
2004
IEEE
16 years 5 months ago
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...
ICML
2003
IEEE
16 years 5 months ago
Principled Methods for Advising Reinforcement Learning Agents
An important issue in reinforcement learning is how to incorporate expert knowledge in a principled manner, especially as we scale up to real-world tasks. In this paper, we presen...
Eric Wiewiora, Garrison W. Cottrell, Charles Elkan
ICML
2001
IEEE
16 years 5 months ago
Direct Policy Search using Paired Statistical Tests
Direct policy search is a practical way to solve reinforcement learning problems involving continuous state and action spaces. The goal becomes finding policy parameters that maxi...
Malcolm J. A. Strens, Andrew W. Moore
ICML
2000
IEEE
16 years 5 months ago
Combining Reinforcement Learning with a Local Control Algorithm
We explore combining reinforcement learning with a hand-crafted local controller in a manner suggested by the chaotic control algorithm of Vincent, Schmitt and Vincent (1994). A c...
Andrew G. Barto, Jette Randløv, Michael T. ...
IDTRUST
2010
ACM
15 years 11 months ago
Computational techniques for increasing PKI policy comprehension by human analysts
Natural-language policies found in X.509 PKI describe an organization’s stated policy as a set of requirements for trust. The widespread use of X.509 underscores the importance ...
Gabriel A. Weaver, Scott A. Rea, Sean W. Smith