Emile van Krieken

Postdoc at University of Edinburgh

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University of Edinburgh

Emile.van.Krieken@ed.ac.uk

I am a postdoc in the NLP group and the APRIL lab at the University of Edinburgh under the ELIAI program. I obtained a PhD with distinction (cum laude) in Artificial Intelligence at the Vrije Universiteit Amsterdam in 2024, where I am also a visiting researcher in the Learning and Reasoning group.

My research combines symbolic reasoning and machine learning, or “Neurosymbolic Learning”. It includes research into differentiable fuzzy logics and optimization with discrete latent variables. I developed the Storchastic PyTorch library, which implements many gradient estimation methods. I recently developed A-NeSI, a highly scalable Neurosymbolic method that uses neural networks for symbolic inference.

I’m also interested in Personal Knowledge Management and developed Juggl, a plugin for Obsidian.md that adds a customizable graph view. Other plugins include Supercharged Links.

I composed some music: You can listen to some songs here!

news

Sep 09, 2024 Our paper on ULLER, a general-purpose NeSy language, is accepted as a spotlight oral presentation at NeSy 2024. In addition, I’m presenting my ICML paper as a spotlight oral, and we are giving a tutorial on ULLER on Thursday the 12th of September.
Jun 27, 2024 I am giving a keynote talk at the Differentiable Almost Everything ICML 2024 workshop.
May 02, 2024 Our paper “On the Independence Assumption in Probabilistic Neurosymbolic Learning” is accepted at ICML 2024.
Apr 26, 2024 Our paper “BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts” is accepted at UAI 2024 as a spotlight paper.
Jan 15, 2024 I successfully defended my PhD thesis at the Vrije Universiteit with Cum Laude distinction (top 5%).

selected publications

  1. NeSy (SPOTLIGHT)
    ULLER: A Unified Language for Learning and Reasoning
    Emile van Krieken, Samy Badreddine, Robin Manhaeve, and 1 more author
    In 18th International Conference on Neural-Symbolic Learning and Reasoning , 2024
  2. NeurIPS
    A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
    Emile van Krieken, Thiviyan Thanapalasingam, Jakub Tomczak, and 2 more authors
    In Advances in Neural Information Processing Systems , 2023
  3. ICML
    On the Independence Assumption in Neurosymbolic Learning
    Emile van Krieken, Pasquale Minervini, Edoardo M Ponti, and 1 more author
    In International Conference on Machine Learning , 2024
  4. AI Journal
    Analyzing Differentiable Fuzzy Logic Operators
    Emile van Krieken, Erman Acar, and Frank Harmelen
    In Artificial Intelligence , 2022
  5. NeurIPS
    Storchastic: A Framework for General Stochastic Automatic Differentiation
    Emile van Krieken, Jakub Tomczak, and Annette Ten Teije
    In Advances in Neural Information Processing Systems , 2021