
Automating Data Science: Prospects and Challenges
Given the complexity of typical data science projects and the associated...
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Leaving Goals on the Pitch: Evaluating Decision Making in Soccer
Analysis of the popular expected goals (xG) metric in soccer has determi...
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Discovering Textual Structures: Generative Grammar Induction using Template Trees
Natural language generation provides designers with methods for automati...
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HumanMachine Collaboration for Democratizing Data Science
Everybody wants to analyse their data, but only few posses the data scie...
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From Statistical Relational to NeuroSymbolic Artificial Intelligence
Neurosymbolic and statistical relational artificial intelligence both i...
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Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring
Robotic agents should be able to learn from subsymbolic sensor data, an...
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Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations
Humanrobot interaction often occurs in the form of instructions given f...
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Semantic Relational Object Tracking
This paper addresses the topic of semantic world modeling by conjoining ...
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Knowledge Compilation with Continuous Random Variables and its Application in Hybrid Probabilistic Logic Programming
In probabilistic reasoning, the traditionally discrete domain has been e...
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Automating Personnel Rostering by Learning Constraints Using Tensors
Many problems in operations research require that constraints be specifi...
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DeepProbLog: Neural Probabilistic Logic Programming
We introduce DeepProbLog, a probabilistic logic programming language tha...
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Sketched Answer Set Programming
Answer Set Programming (ASP) is a powerful modeling formalism for combin...
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Flexible constrained sampling with guarantees for pattern mining
Pattern sampling has been proposed as a potential solution to the infamo...
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Semiring Programming: A Framework for Search, Inference and Learning
To solve hard problems, AI relies on a variety of disciplines such as lo...
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The Inductive Constraint Programming Loop
Constraint programming is used for a variety of realworld optimisation ...
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Inference and learning in probabilistic logic programs using weighted Boolean formulas
Probabilistic logic programs are logic programs in which some of the fac...
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kLog: A Language for Logical and Relational Learning with Kernels
We introduce kLog, a novel approach to statistical relational learning. ...
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Inference in Probabilistic Logic Programs using Weighted CNF's
Probabilistic logic programs are logic programs in which some of the fac...
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Luc De Raedt
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