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Found 15 papers, 2 papers with code
On Computing Probabilistic Abductive Explanations
One solution is to consider intrinsic interpretability, which does not exhibit the drawback of unsoundness.
Feature Necessity & Relevancy in ML Classifier Explanations
Given a machine learning (ML) model and a prediction, explanations can be defined as sets of features which are sufficient for the prediction.
Provably Precise, Succinct and Efficient Explanations for Decision Trees
The paper proposes two logic encodings for computing smallest {\delta}-relevant sets for DTs.
Efficient Explanations for Knowledge Compilation Languages
Knowledge compilation (KC) languages find a growing number of practical uses, including in Constraint Programming (CP) and in Machine Learning (ML).
Efficient Explanations With Relevant Sets
Recent work proposed $\delta$-relevant inputs (or sets) as a probabilistic explanation for the predictions made by a classifier on a given input.
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay
In contrast, we show that the computation of one PI-explanation for an NBC can be achieved in log-linear time, and that the same result also applies to the more general class of linear classifiers.
Strengthening neighbourhood substitution
Domain reduction is an essential tool for solving the constraint satisfaction problem (CSP).
Steepest ascent can be exponential in bounded treewidth problems
We investigate the complexity of local search based on steepest ascent.
On Singleton Arc Consistency for CSPs Defined by Monotone Patterns
Singleton arc consistency is an important type of local consistency which has been recently shown to solve all constraint satisfaction problems (CSPs) over constraint languages of bounded width.
Simple epistemic planning: generalised gossiping
The gossip problem, in which information (known as secrets) must be shared among a certain number of agents using the minimum number of calls, is of interest in the conception of communication networks and protocols.
The Power of Arc Consistency for CSPs Defined by Partially-Ordered Forbidden Patterns
Characterising tractable fragments of the constraint satisfaction problem (CSP) is an important challenge in theoretical computer science and artificial intelligence.
Variable and value elimination in binary constraint satisfaction via forbidden patterns
Variable or value elimination in a constraint satisfaction problem (CSP) can be used in preprocessing or during search to reduce search space size.
On Backdoors To Tractable Constraint Languages
In the context of CSPs, a strong backdoor is a subset of variables such that every complete assignment yields a residual instance guaranteed to have a specified property.
Tractable Triangles and Cross-Free Convexity in Discrete Optimisation
The minimisation problem of a sum of unary and pairwise functions of discrete variables is a general NP-hard problem with wide applications such as computing MAP configurations in Markov Random Fields (MRF), minimising Gibbs energy, or solving binary Valued Constraint Satisfaction Problems (VCSPs).
The tractability of CSP classes defined by forbidden patterns
We show that this framework generalises relational properties and allows us to capture known hybrid tractable classes.
