
On Symbolically Encoding the Behavior of Random Forests
Recent work has shown that the inputoutput behavior of some machine lea...
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A New Perspective on Learning ContextSpecific Independence
Local structure such as contextspecific independence (CSI) has received...
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On Tractable Representations of Binary Neural Networks
We consider the compilation of a binary neural network's decision functi...
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On the Relative Expressiveness of Bayesian and Neural Networks
A neural network computes a function. A central property of neural netwo...
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A Symbolic Approach to Explaining Bayesian Network Classifiers
We propose an approach for explaining Bayesian network classifiers, whic...
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On Relaxing Determinism in Arithmetic Circuits
The past decade has seen a significant interest in learning tractable pr...
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Dual Decomposition from the Perspective of Relax, Compensate and then Recover
Relax, Compensate and then Recover (RCR) is a paradigm for approximate i...
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Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data
We propose an efficient family of algorithms to learn the parameters of ...
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New Advances and Theoretical Insights into EDML
EDML is a recently proposed algorithm for learning MAP parameters in Bay...
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Lifted Relax, Compensate and then Recover: From Approximate to Exact Lifted Probabilistic Inference
We propose an approach to lifted approximate inference for firstorder p...
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On Bayesian Network Approximation by Edge Deletion
We consider the problem of deleting edges from a Bayesian network for th...
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EDML: A Method for Learning Parameters in Bayesian Networks
We propose a method called EDML for learning MAP parameters in binary Ba...
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Arthur Choi
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