Luc De Raedt
40 papers · 2006–2026 · 12 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (12) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (10) π§ Keyword Pioneer π Academic Marathon (19)
π
Academic Marathon
(19)
π
Cross-Pollinator
(15)
π
Renaissance Researcher
(9)
π§¬
Topic Evolution
π
Keyword Champion
(4)
π
Trend Setter
π₯
Unstoppable
(9)
ποΈ
Keyword Collector
(145)
β‘
Prolific Year
(5)
π
Century Club
(39)
π
Conference Pioneer
Conferences
IJCAI (15)
AAAI (11)
EMNLP (2)
JMLR (2)
NIPS (2)
UAI (2)
ACL (1)
COLING (1)
CONLL (1)
ICCV (1)
ICML (1)
NAACL (1)
Top co-authors
Keywords
neurosymbolic ai
(4)
constraint learning
(4)
probabilistic logic
(4)
bayesian inference
(3)
probabilistic inference
(3)
knowledge compilation
(3)
probabilistic logic programming
(3)
weighted model integration
(3)
inductive logic programming
(2)
reinforcement learning
(2)
weighted model counting
(2)
probabilistic programming
(2)
relational learning
(2)
machine learning
(2)
symbolic reasoning
(2)
combinatorial optimization
(2)
markov decision process
(2)
constraint satisfaction
(2)
logical reasoning
(2)
probabilistic reasoning
(2)
Papers
DeepProofLog: Efficient Proving in Deep Stochastic Logic Programs
AAAI 2026
NeSyA: Neurosymbolic Automata
IJCAI 2025
Relational Neurosymbolic Markov Models
AAAI 2025
The Gradient of Algebraic Model Counting
AAAI 2025
Neurosymbolic Reinforcement Learning: Playing MiniHack with Probabilistic Logic Shields
AAAI 2025
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments
COLING 2024
On the Hardness of Probabilistic Neurosymbolic Learning
ICML 2024
SayCanPay: Heuristic Planning with Large Language Models Using Learnable Domain Knowledge
AAAI 2024
Inference and Learning in Dynamic Decision Networks Using Knowledge Compilation
AAAI 2024
Neural probabilistic logic programming in discrete-continuous domains
UAI 2023
Soft-Unification in Deep Probabilistic Logic
NIPS 2023
Safe Reinforcement Learning via Probabilistic Logic Shields
IJCAI 2023
DeepStochLog: Neural Stochastic Logic Programming
AAAI 2022
Inference and Learning with Model Uncertainty in Probabilistic Logic Programs
AAAI 2022
Democratizing Constraint Satisfaction Problems through Machine Learning
AAAI 2021
Learning CNF Theories Using MDL and Predicate Invention
IJCAI 2021
Mapping probability word problems to executable representations
EMNLP 2021
ProbAnch: a Modular Probabilistic Anchoring Framework
IJCAI 2020
From Statistical Relational to Neuro-Symbolic Artificial Intelligence
IJCAI 2020
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation
AAAI 2020
The pywmi Framework and Toolbox for Probabilistic Inference using Weighted Model Integration
IJCAI 2019
Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation
AAAI 2019
How to Exploit Structure while Solving Weighted Model Integration Problems
UAI 2019
Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations
NAACL 2019
Acquiring Integer Programs from Data
IJCAI 2019
DeepProbLog: Neural Probabilistic Logic Programming
NIPS 2018
Learning SMT(LRA) Constraints using SMT Solvers
IJCAI 2018
Solving Probability Problems in Natural Language
IJCAI 2017
Stochastic Constraint Programming with And-Or Branch-and-Bound
IJCAI 2017
kLog: A Language for Logical and Relational Learning with Kernels (Extended Abstract)
IJCAI 2015
Graph Invariant Kernels
IJCAI 2015
Anytime Inference in Probabilistic Logic Programs with Tp-Compilation
IJCAI 2015
Inducing Probabilistic Relational Rules from Probabilistic Examples
IJCAI 2015
kLogNLP: Graph Kernelβbased Relational Learning of Natural Language
ACL 2014
Allocentric Pose Estimation
ICCV 2013
MiningZinc: A Modeling Language for Constraint-based Mining
IJCAI 2013
A Statistical Relational Learning Approach to Identifying Evidence Based Medicine Categories
EMNLP 2012
A Statistical Relational Learning Approach to Identifying Evidence Based Medicine Categories
CONLL 2012
Integrating NaΓΒ―ve Bayes and FOIL
JMLR 2007
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting
JMLR 2006