
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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Three Modern Roles for Logic in AI
We consider three modern roles for logic in artificial intelligence, whi...
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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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An Advance on Variable Elimination with Applications to TensorBased Computation
We present new results on the classical algorithm of variable eliminatio...
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On The Reasons Behind Decisions
Recent work has shown that some common machine learning classifiers can ...
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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 Compiling DNNFs without Determinism
Stateoftheart knowledge compilers generate deterministic subsets of D...
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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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HumanLevel Intelligence or AnimalLike Abilities?
The vision systems of the eagle and the snake outperform everything that...
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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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When do Numbers Really Matter?
Common wisdom has it that small distinctions in the probabilities quanti...
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Query DAGs: A Practical Paradigm for Implementing Belief Network Inference
We describe a new paradigm for implementing inference in belief networks...
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On the Role of Canonicity in Bottomup Knowledge Compilation
We consider the problem of bottomup compilation of knowledge bases, whi...
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Skolemization for Weighted FirstOrder Model Counting
Firstorder model counting emerged recently as a novel reasoning task, a...
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On the Complexity and Approximation of Binary Evidence in Lifted Inference
Lifted inference algorithms exploit symmetries in probabilistic models t...
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ObjectionBased Causal Networks
This paper introduces the notion of objectionbased causal networks whic...
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Argument Calculus and Networks
A major reason behind the success of probability calculus is that it pos...
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On the Relation between Kappa Calculus and Probabilistic Reasoning
We study the connection between kappa calculus and probabilistic reasoni...
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Action Networks: A Framework for Reasoning about Actions and Change under Uncertainty
This work proposes action networks as a semantically wellfounded framew...
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A Standard Approach for Optimizing Belief Network Inference using Query DAGs
This paper proposes a novel, algorithmindependent approach to optimizin...
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Dynamic Jointrees
It is well known that one can ignore parts of a belief network when comp...
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Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (2002)
This is the Proceedings of the Eighteenth Conference on Uncertainty in A...
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AnySpace Probabilistic Inference
We have recently introduced an anyspace algorithm for exact inference i...
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A Differential Approach to Inference in Bayesian Networks
We present a new approach for inference in Bayesian networks, which is m...
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Approximating MAP using Local Search
MAP is the problem of finding a most probable instantiation of a set of ...
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Solving MAP Exactly using Systematic Search
MAP is the problem of finding a most probable instantiation of a set of ...
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Reasoning about Bayesian Network Classifiers
Bayesian network classifiers are used in many fields, and one common cla...
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New Advances in Inference by Recursive Conditioning
Recursive Conditioning (RC) was introduced recently as the first anyspa...
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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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Sensitivity Analysis in Bayesian Networks: From Single to Multiple Parameters
Previous work on sensitivity analysis in Bayesian networks has focused o...
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Exploiting Evidence in Probabilistic Inference
We define the notion of compiling a Bayesian network with evidence and 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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Compilation of Propositional Weighted Bases
In this paper, we investigate the extent to which knowledge compilation ...
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On the tractable counting of theory models and its application to belief revision and truth maintenance
We introduced decomposable negation normal form (DNNF) recently as a tra...
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Adnan Darwiche
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Professor and Chairman of the computer science department at UCLA, M.S. (1989) and Ph.D. (1993) degrees in computer science from Stanford University, Director of Automated Reasoning Group at UCLA.