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Accepted Papers

Journal Track Papers

The journal track papers are papers that are currently either fully accepted, or accepted subject to minor revisions at the Special Issue on Learning & Reasoning by the Machine Learning Journal. We will try to provide pre-prints (or links for those published under open access) for most of them. All journal track papers have an oral presentation slot in the program.

Ashwin Srinivasan, Michael Bain and A. Baskar
Learning Explanations for Biological Feedback with Delays Using an Event Calculus

Tony Ribeiro, Maxime Folschette, Morgan Magnin, Katsumi Inoue
Learning Any Memoryless Discrete Semantics for Dynamical Systems Represented by Logic Programs (pdf)

Kun Gao, Hanpin Wang, Yongzhi Cao and Katsumi Inoue
Learning from Interpretation Transition Using Differentiable Logic Programming Semantics (pdf)

Gustav Sourek, Filip Zelezny and Ondrej Kuzelka
Beyond Graph Neural Networks with Lifted Relational Neural Networks (pdf)

Varun Embar, Sriram Srinivasan and Lise Getoor
A Comparison of Statistical Relational Learning and Graph Neural Networks for Aggregate Graph Queries (pdf)

Tirtharaj Dash, Ashwin Srinivasan and Anguraj Baskar
Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment (pdf)

Lun Ai, Stephen H. Muggleton, Céline Hocquette, Mark Gromowski, Ute Schmid
Beneficial and Harmful Explanatory Machine Learning (pdf)

Stassa Patsantzis, Stephen H. Muggleton
Top Program Construction and Reduction for Polynomial Time Meta-Interpretive Learning (pdf)

Johannes Rabold, Michael Siebers, Ute Schmid
Generating Contrastive Explanations for Inductive Logic Programming Based on a Near Miss Approach (pdf)

Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig
Incorporating Symbolic Domain Knowledge into Graph Neural Networks (pdf)

Michele Fraccaroli, Evelina Lamma, Fabrizio Riguzzi
Symbolic DNN-Tuner (pdf)

Gust Verbruggen, Sumit Gulwani and Luc De Raedt
FlashBack: Semantic Programming by Example

Lidia Contreras-Ochando, Cèsar Ferri and José Hernández-Orallo
AUTOMAT[R]IX: Learning Simple Matrix Pipelines (pdf)

Jiaoyan Chen, Pan Hu, Ernesto Jimenez-Ruiz, Ole Magnus Holter, Denvar Antonyrajah and Ian Horrocks
OWL2Vec*: Embedding of OWL Ontologies (pdf)

Ludovico Mitchener, David Tuckey, Matthew Crosby and Alessandra Russo
Detect, Understand, Act: A Neuro-Symbolic Hierarchical Reinforcement Learning Framework (pdf)

Wen-Chi Yang, Jean-Francois Raskin and Luc De Raedt
Lifted Model Checking for Relational MDPs (pdf)

Blaž Škrlj, Matej Martinc, Nada Lavrač & Senja Pollak
autoBOT: evolving neuro‑symbolic representations for explainable low resource text classifcation (pdf)

Andrew Cropper and Rolf Morel
Learning Programs by Learning from Failures (pdf)

Alice Tarzariol, Martin Gebser, Konstantin Schekotihin
Lifting Symmetry Breaking Constraints with Inductive Logic Programming (pdf)

Daniele Meli, Mohan Sridharan, Paolo Fiorini
Inductive Learning of Answer Set Programs for Autonomous Surgical Task Planning - Application to a Training Task for Surgeons (pdf)

Fabrizio Riguzzi, Elena Bellodi, Riccardo Zese, Marco Alberti and Evelina Lamma
Probabilistic Inductive Constraint Logic (pdf)

Arnaud Nguembang Fadja, Fabrizio Riguzzi and Evelina Lamma
Learning Hierarchical Probabilistic Logic Programs (pdf)

Conference Track Papers

Papers accepted by one of the workshops that participate in IJCLR 2021. Papers that appear below and do not appear in the program page will be presented as posters only.


Cristina Cornelio and Veronika Thost
Synthetic Datasets and Evaluation Tools for Inductive Neural Reasoning (pdf)

Devendra Dhami, Siwen Yan, Gautam Kunapuli and Sriraam Natarajan
Non-Parametric Learning of Embeddings for Relational Data using Gaifman Locality Theorem (pdf)

Fabrizio Ventola, Devendra Dhami and Kristian Kersting
Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits (pdf)

Florian Andreas Marwitz, Tanya Braun and Ralf Möller
A First Step Towards Even More Sparse Encodings of Probability Distributions(pdf)

Hien Nguyen and Chiaki Sakama
Feature learning by least generalization(pdf)

Victor Guimarães and Vítor Costa
Online Learning of Logic Based Neural Network Structures (pdf)

Thais Luca, Aline Paes and Gerson Zaverucha
Mapping Across Relational Domains for Transfer Learning with Word Embeddings-based Similarity (pdf)

Didac Barroso-Bergada, Alireza Tamaddoni-Nezhad, Stephen H. Muggleton, Corinne Vacher, Nika Galic and David A. Bohan
Machine learning of microbial interactions using Abductive ILP and Hypothesis Frequency/Compression Estimation (pdf)

Yin Jun Phua and Katsumi Inoue
Learning Logic Programs Using Neural Networks by Exploiting Symbolic Invariance (pdf)

Patrick Westphal, Sahar Vahdati and Jens Lehmann
A Simulated Annealing Meta-heuristic for Concept Learning in Description Logics (pdf)

Dany Varghese, Roman Bauer, Daniel Baxter-Beard, Stephen Muggleton and Alireza Tamaddoni-Nezhad
Human-like rule learning from images using one-shot hypothesis derivation (pdf)

Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig and Arijit Roy
Using Domain-Knowledge to Assist Lead Discovery in Early-Stage Drug Design (pdf)

Leticia Figueiredo, Aline Paes and Gerson Zaverucha Transfer Learning for Boosted Relational Dependency Networks Through a Genetic Algorithm (pdf)

Oliver Ray
Learning and revising dynamic temporal theories in the full Discrete Event Calculus (pdf)

Can Erten and Dimitar Kazakov
Ontology Graph Embeddings and ILP for Financial Forecasting (pdf)

Jáchym Barvínek, Timothy van Bremen, Yuyi Wang, Filip Železný and Ondřej Kuželka
Automatic Conjecturing of P-RecursionsUsing Lifted Inference (pdf)

Claudia d’Amato, Nicola Flavio Quatraro and Nicola Fanizzi
Embedding Models for Knowledge Graphs Induced by Clusters of Relations and Background Knowledge (pdf)

Leopoldo Bertossi and Gabriela Reyes
Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classification (pdf)

Omar Iken, Maxime Folschette and Tony Ribeiro
Automatic Modeling of Dynamical Interactions Within Marine Ecosystems (pdf)

Tony Ribeiro, Maxime Folschette, Morgan Magnin and Katsumi Inoue
Polynomial Algorithm For Learning From Interpretation Transition (pdf)

Mark Gromowski, Dennis Müller and Ute Schmid
Exploiting Temporal Relations of Events for Classification Tasks – Proof of Concept Study with Synthetic Data Sets (pdf)

Rasmus Larsen and Mikkel Nørgaard Schmidt
Programmatic policy extraction by iterative local search (pdf)

Sharad Chitlangia, Atharv Sonwane, Tirtharaj Dash, Lovekesh Vig, Ashwin Srinivasan and Gautam Shroff
Using Program Synthesis and Inductive Logic Programming to solve Bongard Problems (work in progress report) (pdf)

NeSy Papers

Proceedings available from the NeSy web site

StarAI Papers

Tanya Braun, Stefan Fischer, Florian Lau and Ralf Möller
Lifting DecPOMDPs for Nanoscale Systems - A Work in Progress (pdf)

Marcel Gehrke
On the Completness and Complexity of the Lifted Dynamic Junction Tree Algorithm (pdf)

Simon Vandevelde, Victor Verreet, Luc De Raedt and Joost Vennekens
A Table-Based Representation for Probabilistic Logic: Preliminary Results (pdf)

Robin Manhaeve, Giuseppe Marra and Luc De Raedt
Approximate Inference for Neural Probabilistic Logic Programming (pdf)

Sagar Malhotra and Luciano Serafini
Weighted Model Counting in FO2 with Cardinality Constraints and Counting Quantifiers: A Closed Form Formula (pdf)

Pietro Totis, Angelika Kimmig and Luc De Raedt
SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation (pdf)

Carl Allen, Ivana Balazevic and Timothy Hospedales
Interpreting Knowledge Graph Relation Representation from Word Embeddings (pdf)

Carl Giovanni De Toni, Luca Erculiani and Andrea Passerini
Learning compositional programs with arguments and sampling (pdf)

Devendra Dhami, Siwen Yan and Sriraam Natarajan
A Statistical Relational Approach to Learning Distance-based GCNs (pdf)

Eda Bayram
Propagation on Multi-relational Graphs for Node Regression (pdf)

Yuqiao Chen, Nicholas Ruozzi and Sriraam Natarajan
Relational Neural Markov Random Fields (pdf)

Andrea Galassi, Marco Lippi and Paolo Torroni
Investigating Logic Tensor Networks for Neural-Symbolic Argument Mining (pdf)

Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran and Prasad Tadepalli
Dynamic probabilistic logic models for effective abstractions in RL (pdf)

Richard Mar and Oliver Schulte
Pre and Post Counting Approaches for Scalable Statistical-Relational Model Discovery (pdf)

Marc Roig Vilamala, Tianwei Xing, Harrison Taylor, Luis Garcia, Mani Srivastava, Lance Kaplan, Alun Preece, Angelika Kimmig and Federico Cerutti
Using DeepProbLog to perform Complex Event Processing on an Audio Stream (pdf)

Recently Published Papers

Felix Weitkämper
An asymptotic analysis of probabilistic logic programming, with implications for expressing projective families of distributions (TPLP 2021) (pdf)

Damiano Azzolini, Fabrizio Riguzzi and Evelina Lamma
A Semantics for Hybrid Probabilistic Logic Programs with Function Symbols (AIJ 2021) (pdf)

Mark Law, Alessandra Russo, Krysia Broda and Elisa Bertino
Scalable Non-observational Predicate Learning in ASP (IJCAI 2021) (pdf)

Pasquale Minervini, Daniel Daza, Erik Arakelyan and Michael Cochez
Complex Query Answering with Neural Link Predictors (ICLR 2021) (pdf)

Helge Spieker
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-Interaction (IJCNN 2021) (pdf)