Benefits of Social Learning in Physical Robots. - Jacqueline Heinerman; Bart Bussmann; Rick Groenendijk; Emile van Krieken; Jesper Slik; Alessandro Tezza; Evert Haasdijk; A. E. Eiben (SSCI, 2018)

Semi-supervised Learning using Differentiable Reasoning. - Emile van Krieken; Erman Acar; Frank van Harmelen (FLAP, 2019)

Analyzing Differentiable Fuzzy Implications. - Emile van Krieken; Erman Acar; Frank van Harmelen (KR, 2020)

Storchastic: A Framework for General Stochastic Automatic Differentiation. - Emile van Krieken; Jakub M. Tomczak; Annette ten Teije (NeurIPS, 2021)

Prompting as Probing: Using Language Models for Knowledge Base Construction. - Dimitrios Alivanistos; Selene Baez Santamaría; Michael Cochez; Jan-Christoph Kalo; Emile van Krieken; Thiviyan Thanapalasingam (LM-KBC@ISWC, 2022)

Analysis of Measure-Valued Derivatives in a Reinforcement Learning Actor-Critic Framework. - Kim van den Houten; Emile van Krieken; Bernd Heidergott (WSC, 2022)

Analyzing Differentiable Fuzzy Logic Operators. - Emile van Krieken; Erman Acar; Frank van Harmelen (Artif. Intell., 2022)

A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference. - Emile van Krieken; Thiviyan Thanapalasingam; Jakub M. Tomczak; Frank van Harmelen; Annette ten Teije (NeurIPS, 2023)

IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation. - Thiviyan Thanapalasingam; Emile van Krieken; Peter Bloem; Paul Groth (CoRR, 2023)

IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation. - Thiviyan Thanapalasingam; Emile van Krieken; Peter Bloem; Paul Groth (Zenodo, 2023)

IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation. - Thiviyan Thanapalasingam; Emile van Krieken; Peter Bloem; Paul Groth (Zenodo, 2023)

IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation. - Thiviyan Thanapalasingam; Emile van Krieken; Peter Bloem; Paul Groth (Zenodo, 2023)

IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation. - Thiviyan Thanapalasingam; Emile van Krieken; Peter Bloem; Paul Groth (Zenodo, 2023)

Refining neural network predictions using background knowledge. - Alessandro Daniele; Emile van Krieken; Luciano Serafini; Frank van Harmelen (Mach. Learn., 2023)

On the Independence Assumption in Neurosymbolic Learning. - Emile van Krieken; Pasquale Minervini; Edoardo M. Ponti; Antonio Vergari (ICML, 2024)

ULLER: A Unified Language for Learning and Reasoning. - Emile van Krieken; Samy Badreddine; Robin Manhaeve; Eleonora Giunchiglia (NeSy (1), 2024)

A Neuro-Symbolic Benchmark Suite for Concept Quality and Reasoning Shortcuts. - Samuele Bortolotti; Emanuele Marconato; Tommaso Carraro; Paolo Morettin; Emile van Krieken; Antonio Vergari; Stefano Teso; Andrea Passerini (NeurIPS, 2024)

BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts. - Emanuele Marconato; Samuele Bortolotti; Emile van Krieken; Antonio Vergari; Andrea Passerini; Stefano Teso (UAI, 2024)

Optimisation in Neurosymbolic Learning Systems. - Emile van Krieken (CoRR, 2024)

A Benchmark Suite for Systematically Evaluating Reasoning Shortcuts. - Samuele Bortolotti; Emanuele Marconato; Tommaso Carraro; Paolo Morettin; Emile van Krieken; Antonio Vergari; Stefano Teso; Andrea Passerini (CoRR, 2024)

A Benchmark Suite for Systematically Evaluating Reasoning Shortcuts. - Samuele Bortolotti; Emanuele Marconato; Tommaso Carraro; Paolo Morettin; Emile van Krieken; Antonio Vergari; Stefano Teso; Andrea Passerini (Zenodo, 2024)

Mixtures of In-Context Learners. - Giwon Hong; Emile van Krieken; Edoardo Maria Ponti; Nikolay Malkin; Pasquale Minervini (ACL (1), 2025)

Are We Done with MMLU? - Aryo Pradipta Gema; Joshua Ong Jun Leang; Giwon Hong; Alessio Devoto; Alberto Carlo Maria Mancino; Rohit Saxena; Xuanli He; Yu Zhao 043; Xiaotang Du; Mohammad Reza Ghasemi Madani; Claire Barale; Robert McHardy; et al. (NAACL (Long Papers), 2025)

Self-Training Large Language Models for Tool-Use Without Demonstrations. - Ne Luo; Aryo Pradipta Gema; Xuanli He; Emile van Krieken; Pietro Lesci; Pasquale Minervini (NAACL (Findings), 2025)

GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks. - Taraneh Younesian; Daniel Daza; Emile van Krieken; Thiviyan Thanapalasingam; Peter Bloem (Trans. Mach. Learn. Res., 2025)

Noise to the Rescue: Escaping Local Minima in Neurosymbolic Local Search. - Alessandro Daniele; Emile van Krieken (CoRR, 2025)

Neurosymbolic Diffusion Models. - Emile van Krieken; Pasquale Minervini; Edoardo M. Ponti; Antonio Vergari (CoRR, 2025)

Breaking Rank Bottlenecks in Knowledge Graph Completion. - Samy Badreddine; Emile van Krieken; Luciano Serafini (CoRR, 2025)

Neurosymbolic Reasoning Shortcuts under the Independence Assumption. - Emile van Krieken; Pasquale Minervini; Edoardo Maria Ponti; Antonio Vergari (CoRR, 2025)