Adnan Darwiche

research

∙ 03/20/2022

On the Computation of Necessary and Sufficient Explanations

The complete reason behind a decision is a Boolean formula that characte...

Adnan Darwiche, et al. ∙

research

∙ 02/07/2022

Tractable Boolean and Arithmetic Circuits

Tractable Boolean and arithmetic circuits have been studied extensively ...

Adnan Darwiche, et al. ∙

research

∙ 02/07/2022

Causal Inference Using Tractable Circuits

The aim of this paper is to discuss a recent result which shows that pro...

Adnan Darwiche, et al. ∙

research

∙ 08/23/2021

On Quantifying Literals in Boolean Logic and Its Applications to Explainable AI

Quantified Boolean logic results from adding operators to Boolean logic ...

Adnan Darwiche, et al. ∙

research

∙ 07/03/2020

On Symbolically Encoding the Behavior of Random Forests

Recent work has shown that the input-output behavior of some machine lea...

Arthur Choi, et al. ∙

research

∙ 06/12/2020

A New Perspective on Learning Context-Specific Independence

Local structure such as context-specific independence (CSI) has received...

Yujia Shen, et al. ∙

research

∙ 04/18/2020

Three Modern Roles for Logic in AI

We consider three modern roles for logic in artificial intelligence, whi...

Adnan Darwiche, et al. ∙

research

∙ 04/05/2020

On Tractable Representations of Binary Neural Networks

We consider the compilation of a binary neural network's decision functi...

Weijia Shi, et al. ∙

research

∙ 02/21/2020

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

We present new results on the classical algorithm of variable eliminatio...

Adnan Darwiche, et al. ∙

research

∙ 02/21/2020

On The Reasons Behind Decisions

Recent work has shown that some common machine learning classifiers can ...

Adnan Darwiche, et al. ∙

research

∙ 12/21/2018

On the Relative Expressiveness of Bayesian and Neural Networks

A neural network computes a function. A central property of neural netwo...

Arthur Choi, et al. ∙

research

∙ 05/09/2018

A Symbolic Approach to Explaining Bayesian Network Classifiers

We propose an approach for explaining Bayesian network classifiers, whic...

Andy Shih, et al. ∙

research

∙ 09/20/2017

On Compiling DNNFs without Determinism

State-of-the-art knowledge compilers generate deterministic subsets of D...

Umut Oztok, et al. ∙

research

∙ 08/22/2017

On Relaxing Determinism in Arithmetic Circuits

The past decade has seen a significant interest in learning tractable pr...

Arthur Choi, et al. ∙

research

∙ 07/13/2017

Human-Level Intelligence or Animal-Like Abilities?

The vision systems of the eagle and the snake outperform everything that...

Adnan Darwiche, et al. ∙

research

∙ 04/05/2015

Dual Decomposition from the Perspective of Relax, Compensate and then Recover

Relax, Compensate and then Recover (RCR) is a paradigm for approximate i...

Arthur Choi, et al. ∙

research

∙ 08/07/2014

When do Numbers Really Matter?

Common wisdom has it that small distinctions in the probabilities quanti...

Hei Chan, et al. ∙

research

∙ 08/07/2014

Query DAGs: A Practical Paradigm for Implementing Belief Network Inference

We describe a new paradigm for implementing inference in belief networks...

Adnan Darwiche, et al. ∙

research

∙ 04/15/2014

On the Role of Canonicity in Bottom-up Knowledge Compilation

We consider the problem of bottom-up compilation of knowledge bases, whi...

Guy Van den Broeck, et al. ∙

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∙ 12/19/2013

Skolemization for Weighted First-Order Model Counting

First-order model counting emerged recently as a novel reasoning task, a...

Guy Van den Broeck, et al. ∙

research

∙ 11/26/2013

On the Complexity and Approximation of Binary Evidence in Lifted Inference

Lifted inference algorithms exploit symmetries in probabilistic models t...

Guy Van den Broeck, et al. ∙

research

∙ 03/13/2013

Objection-Based Causal Networks

This paper introduces the notion of objection-based causal networks whic...

Adnan Darwiche, et al. ∙

research

∙ 03/06/2013

Argument Calculus and Networks

A major reason behind the success of probability calculus is that it pos...

Adnan Darwiche, et al. ∙

research

∙ 02/27/2013

On the Relation between Kappa Calculus and Probabilistic Reasoning

We study the connection between kappa calculus and probabilistic reasoni...

Adnan Darwiche, et al. ∙

research

∙ 02/27/2013

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

This work proposes action networks as a semantically well-founded framew...

Adnan Darwiche, et al. ∙

research

∙ 02/06/2013

A Standard Approach for Optimizing Belief Network Inference using Query DAGs

This paper proposes a novel, algorithm-independent approach to optimizin...

Adnan Darwiche, et al. ∙

research

∙ 01/30/2013

Dynamic Jointrees

It is well known that one can ignore parts of a belief network when comp...

Adnan Darwiche, et al. ∙

research

∙ 01/19/2013

Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (2002)

This is the Proceedings of the Eighteenth Conference on Uncertainty in A...

Adnan Darwiche, et al. ∙

research

∙ 01/16/2013

Any-Space Probabilistic Inference

We have recently introduced an any-space algorithm for exact inference i...

Adnan Darwiche, et al. ∙

research

∙ 01/16/2013

A Differential Approach to Inference in Bayesian Networks

We present a new approach for inference in Bayesian networks, which is m...

Adnan Darwiche, et al. ∙

research

∙ 01/10/2013

Approximating MAP using Local Search

MAP is the problem of finding a most probable instantiation of a set of ...

James D. Park, et al. ∙

research

∙ 10/19/2012

Solving MAP Exactly using Systematic Search

MAP is the problem of finding a most probable instantiation of a set of ...

James D. Park, et al. ∙

research

∙ 10/19/2012

Reasoning about Bayesian Network Classifiers

Bayesian network classifiers are used in many fields, and one common cla...

Hei Chan, et al. ∙

research

∙ 10/19/2012

New Advances in Inference by Recursive Conditioning

Recursive Conditioning (RC) was introduced recently as the first any-spa...

David Allen, et al. ∙

research

∙ 10/16/2012

New Advances and Theoretical Insights into EDML

EDML is a recently proposed algorithm for learning MAP parameters in Bay...

Khaled S. Refaat, et al. ∙

research

∙ 10/16/2012

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...

Guy Van den Broeck, et al. ∙

research

∙ 07/11/2012

Sensitivity Analysis in Bayesian Networks: From Single to Multiple Parameters

Previous work on sensitivity analysis in Bayesian networks has focused o...

Hei Chan, et al. ∙

research

∙ 07/04/2012

Exploiting Evidence in Probabilistic Inference

We define the notion of compiling a Bayesian network with evidence and p...

Mark Chavira, et al. ∙

research

∙ 07/04/2012

On Bayesian Network Approximation by Edge Deletion

We consider the problem of deleting edges from a Bayesian network for th...

Arthur Choi, et al. ∙

research

∙ 02/14/2012

EDML: A Method for Learning Parameters in Bayesian Networks

We propose a method called EDML for learning MAP parameters in binary Ba...

Arthur Choi, et al. ∙

research

∙ 07/11/2002

Compilation of Propositional Weighted Bases

In this paper, we investigate the extent to which knowledge compilation ...

Adnan Darwiche, et al. ∙

research

∙ 03/09/2000

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...

Adnan Darwiche, et al. ∙

Adnan Darwiche

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