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02/27/2023
CrystalBox: Future-Based Explanations for DRL Network Controllers
Lack of explainability is a key factor limiting the practical adoption o...
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02/24/2023
Prioritized Trace Selection: Towards High-Performance DRL-based Network Controllers
Deep Reinforcement Learning (DRL) based controllers offer high performan...
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12/12/2022
On Computing Probabilistic Abductive Explanations
The most widely studied explainable AI (XAI) approaches are unsound. Thi...
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06/20/2022
Eliminating The Impossible, Whatever Remains Must Be True
The rise of AI methods to make predictions and decisions has led to a pr...
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05/19/2022
Provably Precise, Succinct and Efficient Explanations for Decision Trees
Decision trees (DTs) embody interpretable classifiers. DTs have been adv...
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03/07/2022
Scalable Verification of GNN-based Job Schedulers
Recently, Graph Neural Networks (GNNs) have been applied for scheduling ...
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02/28/2022
KL Divergence Estimation with Multi-group Attribution
Estimating the Kullback-Leibler (KL) divergence between two distribution...
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06/01/2021
Efficient Explanations With Relevant Sets
Recent work proposed δ-relevant inputs (or sets) as a probabilistic expl...
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06/01/2021
Explanations for Monotonic Classifiers
In many classification tasks there is a requirement of monotonicity. Con...
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12/21/2020
On Relating 'Why?' and 'Why Not?' Explanations
Explanations of Machine Learning (ML) models often address a 'Why?' ques...
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08/13/2020
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay
Recent work proposed the computation of so-called PI-explanations of Nai...
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03/14/2020
Partial Queries for Constraint Acquisition
Learning constraint networks is known to require a number of membership ...
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09/06/2019
Automating Cluster Management with Weave
Modern cluster management systems like Kubernetes and Openstack grapple ...
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07/04/2019
On Validating, Repairing and Refining Heuristic ML Explanations
Recent years have witnessed a fast-growing interest in computing explana...
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11/26/2018
Abduction-Based Explanations for Machine Learning Models
The growing range of applications of Machine Learning (ML) in a multitud...
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05/25/2018
Automated Verification of Neural Networks: Advances, Challenges and Perspectives
Neural networks are one of the most investigated and widely used techniq...
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02/24/2018
Constrained Image Generation Using Binarized Neural Networks with Decision Procedures
We consider the problem of binary image generation with given properties...
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10/21/2017
Deep Neural Network Approximation using Tensor Sketching
Deep neural networks are powerful learning models that achieve state-of-...
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09/19/2017
Verifying Properties of Binarized Deep Neural Networks
Understanding properties of deep neural networks is an important challen...
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12/19/2016
Simple Black-Box Adversarial Perturbations for Deep Networks
Deep neural networks are powerful and popular learning models that achie...
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05/28/2014
The Computational Impact of Partial Votes on Strategic Voting
In many real world elections, agents are not required to rank all candid...
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01/16/2014
The Complexity of Integer Bound Propagation
Bound propagation is an important Artificial Intelligence technique used...
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06/21/2013
Breaking Symmetry with Different Orderings
We can break symmetry by eliminating solutions within each symmetry clas...
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04/23/2013
How Hard Is It to Control an Election by Breaking Ties?
We study the computational complexity of controlling the result of an el...
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04/22/2013
Three Generalizations of the FOCUS Constraint
The FOCUS constraint expresses the notion that solutions are concentrate...
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04/03/2013
Coalitional Manipulation for Schulze's Rule
Schulze's rule is used in the elections of a large number of organizatio...
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02/07/2013
Possible and Necessary Winner Problem in Social Polls
Social networks are increasingly being used to conduct polls. We introdu...
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07/07/2012
The SeqBin Constraint Revisited
We revisit the SeqBin constraint. This meta-constraint subsumes a number...
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04/17/2012
Eliminating the Weakest Link: Making Manipulation Intractable?
Successive elimination of candidates is often a route to making manipula...
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03/14/2012
Combining Voting Rules Together
We propose a simple method for combining together voting rules that perf...
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01/03/2012
The RegularGcc Matrix Constraint
We study propagation of the RegularGcc global constraint. This ensures t...
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06/27/2011
Manipulation of Nanson's and Baldwin's Rules
Nanson's and Baldwin's voting rules select a winner by successively elim...
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05/27/2011
Complexity of and Algorithms for Borda Manipulation
We prove that it is NP-hard for a coalition of two manipulators to compu...
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03/19/2011
The AllDifferent Constraint with Precedences
We propose AllDiffPrecedence, a new global constraint that combines toge...
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04/15/2010
Propagating Conjunctions of AllDifferent Constraints
We study propagation algorithms for the conjunction of two AllDifferent ...
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09/28/2009
Breaking Generator Symmetry
Dealing with large numbers of symmetries is often problematic. One solut...
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09/24/2009
The Weighted CFG Constraint
We introduce the weighted CFG constraint and propose a propagation algor...
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09/24/2009
Flow-Based Propagators for the SEQUENCE and Related Global Constraints
We propose new filtering algorithms for the SEQUENCE constraint and some...
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09/17/2009
Decomposition of the NVALUE constraint
We study decompositions of NVALUE, a global constraint that can be used ...
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06/29/2009
Restricted Global Grammar Constraints
We investigate the global GRAMMAR constraint over restricted classes of ...
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05/22/2009
Circuit Complexity and Decompositions of Global Constraints
We show that tools from circuit complexity can be used to study decompos...
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05/22/2009