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.

On Symbolically Encoding the Behavior of Random Forests

Recent work has shown that the input-output behavior of some machine lea...
07/03/2020 ∙ by Arthur Choi, et al. ∙ 0 ∙ share

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A New Perspective on Learning Context-Specific Independence

Local structure such as context-specific independence (CSI) has received...
06/12/2020 ∙ by Yujia Shen, et al. ∙ 0 ∙ share

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Three Modern Roles for Logic in AI

We consider three modern roles for logic in artificial intelligence, whi...
04/18/2020 ∙ by Adnan Darwiche, et al. ∙ 24 ∙ share

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On Tractable Representations of Binary Neural Networks

We consider the compilation of a binary neural network's decision functi...
04/05/2020 ∙ by Weijia Shi, et al. ∙ 0 ∙ share

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An Advance on Variable Elimination with Applications to Tensor-Based Computation

We present new results on the classical algorithm of variable eliminatio...
02/21/2020 ∙ by Adnan Darwiche, et al. ∙ 0 ∙ share

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On The Reasons Behind Decisions

Recent work has shown that some common machine learning classifiers can ...
02/21/2020 ∙ by Adnan Darwiche, et al. ∙ 0 ∙ share

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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...
12/21/2018 ∙ by Arthur Choi, et al. ∙ 0 ∙ share

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A Symbolic Approach to Explaining Bayesian Network Classifiers

We propose an approach for explaining Bayesian network classifiers, whic...
05/09/2018 ∙ by Andy Shih, et al. ∙ 0 ∙ share

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On Compiling DNNFs without Determinism

State-of-the-art knowledge compilers generate deterministic subsets of D...
09/20/2017 ∙ by Umut Oztok, et al. ∙ 0 ∙ share

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On Relaxing Determinism in Arithmetic Circuits

The past decade has seen a significant interest in learning tractable pr...
08/22/2017 ∙ by Arthur Choi, et al. ∙ 0 ∙ share

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Human-Level Intelligence or Animal-Like Abilities?

The vision systems of the eagle and the snake outperform everything that...
07/13/2017 ∙ by Adnan Darwiche, et al. ∙ 0 ∙ share

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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...
04/05/2015 ∙ by Arthur Choi, et al. ∙ 0 ∙ share

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When do Numbers Really Matter?

Common wisdom has it that small distinctions in the probabilities quanti...
08/07/2014 ∙ by Hei Chan, et al. ∙ 0 ∙ share

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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...
08/07/2014 ∙ by Adnan Darwiche, et al. ∙ 0 ∙ share

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On the Role of Canonicity in Bottom-up Knowledge Compilation

We consider the problem of bottom-up compilation of knowledge bases, whi...
04/15/2014 ∙ by Guy Van den Broeck, et al. ∙ 0 ∙ share

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Skolemization for Weighted First-Order Model Counting

First-order model counting emerged recently as a novel reasoning task, a...
12/19/2013 ∙ by Guy Van den Broeck, et al. ∙ 0 ∙ share

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On the Complexity and Approximation of Binary Evidence in Lifted Inference

Lifted inference algorithms exploit symmetries in probabilistic models t...
11/26/2013 ∙ by Guy Van den Broeck, et al. ∙ 0 ∙ share

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Objection-Based Causal Networks

This paper introduces the notion of objection-based causal networks whic...
03/13/2013 ∙ by Adnan Darwiche, et al. ∙ 0 ∙ share

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Argument Calculus and Networks

A major reason behind the success of probability calculus is that it pos...
03/06/2013 ∙ by Adnan Darwiche, et al. ∙ 0 ∙ share

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On the Relation between Kappa Calculus and Probabilistic Reasoning

We study the connection between kappa calculus and probabilistic reasoni...
02/27/2013 ∙ by Adnan Darwiche, et al. ∙ 0 ∙ share

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Action Networks: A Framework for Reasoning about Actions and Change under Uncertainty

This work proposes action networks as a semantically well-founded framew...
02/27/2013 ∙ by Adnan Darwiche, et al. ∙ 0 ∙ share

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A Standard Approach for Optimizing Belief Network Inference using Query DAGs

This paper proposes a novel, algorithm-independent approach to optimizin...
02/06/2013 ∙ by Adnan Darwiche, et al. ∙ 0 ∙ share

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Dynamic Jointrees

It is well known that one can ignore parts of a belief network when comp...
01/30/2013 ∙ by Adnan Darwiche, et al. ∙ 0 ∙ share

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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...
01/19/2013 ∙ by Adnan Darwiche, et al. ∙ 0 ∙ share

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Any-Space Probabilistic Inference

We have recently introduced an any-space algorithm for exact inference i...
01/16/2013 ∙ by Adnan Darwiche, et al. ∙ 0 ∙ share

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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...
01/16/2013 ∙ by Adnan Darwiche, et al. ∙ 0 ∙ share

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Approximating MAP using Local Search

MAP is the problem of finding a most probable instantiation of a set of ...
01/10/2013 ∙ by James D. Park, et al. ∙ 0 ∙ share

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Solving MAP Exactly using Systematic Search

MAP is the problem of finding a most probable instantiation of a set of ...
10/19/2012 ∙ by James D. Park, et al. ∙ 0 ∙ share

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Reasoning about Bayesian Network Classifiers

Bayesian network classifiers are used in many fields, and one common cla...
10/19/2012 ∙ by Hei Chan, et al. ∙ 0 ∙ share

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New Advances in Inference by Recursive Conditioning

Recursive Conditioning (RC) was introduced recently as the first any-spa...
10/19/2012 ∙ by David Allen, et al. ∙ 0 ∙ share

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New Advances and Theoretical Insights into EDML

EDML is a recently proposed algorithm for learning MAP parameters in Bay...
10/16/2012 ∙ by Khaled S. Refaat, et al. ∙ 0 ∙ share

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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 first-order p...
10/16/2012 ∙ by Guy Van den Broeck, et al. ∙ 0 ∙ share

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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...
07/11/2012 ∙ by Hei Chan, et al. ∙ 0 ∙ share

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Exploiting Evidence in Probabilistic Inference

We define the notion of compiling a Bayesian network with evidence and p...
07/04/2012 ∙ by Mark Chavira, et al. ∙ 0 ∙ share

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On Bayesian Network Approximation by Edge Deletion

We consider the problem of deleting edges from a Bayesian network for th...
07/04/2012 ∙ by Arthur Choi, et al. ∙ 0 ∙ share

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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...
02/14/2012 ∙ by Arthur Choi, et al. ∙ 0 ∙ share

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Compilation of Propositional Weighted Bases

In this paper, we investigate the extent to which knowledge compilation ...
07/11/2002 ∙ by Adnan Darwiche, et al. ∙ 0 ∙ share

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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...
03/09/2000 ∙ by Adnan Darwiche, et al. ∙ 0 ∙ share

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Adnan Darwiche

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