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05/09/2023
Logic for Explainable AI
A central quest in explainable AI relates to understanding the decisions...
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04/28/2023
A New Class of Explanations for Classifiers with Non-Binary Features
Two types of explanations have received significant attention in the lit...
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02/28/2023
An Algorithm and Complexity Results for Causal Unit Selection
The unit selection problem aims to identify objects, called units, that ...
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11/24/2022
On the Complexity of Counterfactual Reasoning
We study the computational complexity of counterfactual reasoning in rel...
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03/20/2022
On the Computation of Necessary and Sufficient Explanations
The complete reason behind a decision is a Boolean formula that characte...
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02/07/2022
Tractable Boolean and Arithmetic Circuits
Tractable Boolean and arithmetic circuits have been studied extensively ...
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02/07/2022
Causal Inference Using Tractable Circuits
The aim of this paper is to discuss a recent result which shows that pro...
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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 ...
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07/03/2020
On Symbolically Encoding the Behavior of Random Forests
Recent work has shown that the input-output behavior of some machine lea...
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06/12/2020
A New Perspective on Learning Context-Specific Independence
Local structure such as context-specific independence (CSI) has received...
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04/18/2020
Three Modern Roles for Logic in AI
We consider three modern roles for logic in artificial intelligence, whi...
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04/05/2020
On Tractable Representations of Binary Neural Networks
We consider the compilation of a binary neural network's decision functi...
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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...
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02/21/2020
On The Reasons Behind Decisions
Recent work has shown that some common machine learning classifiers can ...
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12/21/2018
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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05/09/2018
A Symbolic Approach to Explaining Bayesian Network Classifiers
We propose an approach for explaining Bayesian network classifiers, whic...
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09/20/2017
On Compiling DNNFs without Determinism
State-of-the-art knowledge compilers generate deterministic subsets of D...
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08/22/2017
On Relaxing Determinism in Arithmetic Circuits
The past decade has seen a significant interest in learning tractable pr...
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07/13/2017
Human-Level Intelligence or Animal-Like Abilities?
The vision systems of the eagle and the snake outperform everything that...
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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...
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08/07/2014
When do Numbers Really Matter?
Common wisdom has it that small distinctions in the probabilities quanti...
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08/07/2014
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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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...
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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...
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11/26/2013
On the Complexity and Approximation of Binary Evidence in Lifted Inference
Lifted inference algorithms exploit symmetries in probabilistic models t...
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03/13/2013
Objection-Based Causal Networks
This paper introduces the notion of objection-based causal networks whic...
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03/06/2013
Argument Calculus and Networks
A major reason behind the success of probability calculus is that it pos...
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02/27/2013
On the Relation between Kappa Calculus and Probabilistic Reasoning
We study the connection between kappa calculus and probabilistic reasoni...
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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...
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02/06/2013
A Standard Approach for Optimizing Belief Network Inference using Query DAGs
This paper proposes a novel, algorithm-independent approach to optimizin...
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01/30/2013
Dynamic Jointrees
It is well known that one can ignore parts of a belief network when comp...
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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...
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01/16/2013
Any-Space Probabilistic Inference
We have recently introduced an any-space algorithm for exact inference i...
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01/16/2013
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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01/10/2013
Approximating MAP using Local Search
MAP is the problem of finding a most probable instantiation of a set of ...
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10/19/2012
Solving MAP Exactly using Systematic Search
MAP is the problem of finding a most probable instantiation of a set of ...
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10/19/2012
Reasoning about Bayesian Network Classifiers
Bayesian network classifiers are used in many fields, and one common cla...
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10/19/2012
New Advances in Inference by Recursive Conditioning
Recursive Conditioning (RC) was introduced recently as the first any-spa...
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10/16/2012
New Advances and Theoretical Insights into EDML
EDML is a recently proposed algorithm for learning MAP parameters in Bay...
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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...
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07/11/2012
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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07/04/2012
Exploiting Evidence in Probabilistic Inference
We define the notion of compiling a Bayesian network with evidence and p...
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07/04/2012
On Bayesian Network Approximation by Edge Deletion
We consider the problem of deleting edges from a Bayesian network for th...
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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...
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07/11/2002
Compilation of Propositional Weighted Bases
In this paper, we investigate the extent to which knowledge compilation ...
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03/09/2000
