19 citations · 24 across the 13 of their papers we have counts for
15 papers
Relational Task Generation Language: A Declarative Specification Framework for Relational Deep Learning
Oleksii Kolesnichenko, Jakub Peleška, Gustav Š\'ır
Relational Deep Learning (RDL) has become a powerful paradigm for learning from multi-tabular data. However, manually defining RDL prediction tasks is a laborious process that freq…
Incremental Evaluation and Training in Relational Deep Learning
Jakub Peleška, Gustav Šír
Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs to enable end-to-end representation learning. However, prevailing RDL evaluation prac…
Universal Encoders for Modular Relational Deep Learning
Jakub Peleška, Gustav Šír
Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs for end-to-end representation learning. While RDL is evolving rapidly, existing appro…
Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modelling
Danial Hooshyar, Gustav Šír, Yeongwook Yang +6
The growing use of artificial intelligence (AI) in education, particularly large language models (LLMs), has increased interest in intelligent tutoring systems. However, LLMs often…
Problems With Large Language Models for Learner Modelling: Why LLMs Alone Fall Short for Responsible Tutoring in K--12 Education
Danial Hooshyar, Yeongwook Yang, Gustav Šíř +4
The rapid rise of large language model (LLM)-based tutors in K--12 education has fostered a misconception that generative models can replace traditional learner modelling for adapt…
Binarizing Physics-Inspired GNNs for Combinatorial Optimization
Martin Krutský, Gustav Šír, Vyacheslav Kungurtsev +1
Physics-inspired graph neural networks (PI-GNNs) have been utilized as an efficient unsupervised framework for relaxing combinatorial optimization problems encoded through a specif…
Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations
Vyacheslav Kungurtsev, Alessandro Di Frenna, Gustav Sir +5
Research, innovation and practical capital investment have been increasing rapidly toward the realization of autonomous physical agents. This includes industrial and service robots…
Task-Agnostic Contrastive Pretraining for Relational Deep Learning
Jakub Peleška, Gustav Šír
Relational Deep Learning (RDL) is an emerging paradigm that leverages Graph Neural Network principles to learn directly from relational databases by representing them as heterogene…
REDELEX: A Framework for Relational Deep Learning Exploration
Jakub Peleška, Gustav Šír
Relational databases (RDBs) are widely regarded as the gold standard for storing structured information. Consequently, predictive tasks leveraging this data format hold significant…
Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the room
Danial Hooshyar, Gustav Šír, Yeongwook Yang +5
Despite significant advancements in AI-driven educational systems and ongoing calls for responsible AI for education, several critical issues remain unresolved -- acting as the ele…
Towards Responsible and Trustworthy Educational Data Mining: Comparing Symbolic, Sub-Symbolic, and Neural-Symbolic AI Methods
Danial Hooshyar, Eve Kikas, Yeongwook Yang +4
Given the demand for responsible and trustworthy AI for education, this study evaluates symbolic, sub-symbolic, and neural-symbolic AI (NSAI) in terms of generalizability and inter…
"Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of "Causal Machine Learning"
Vyacheslav Kungurtsev, Leonardo Christov Moore, Gustav Sir +1
Causal Learning has emerged as a major theme of research in statistics and machine learning in recent years, promising computational techniques to reveal ``true'' causality. In thi…
Transformers Meet Relational Databases
Jakub Peleška, Gustav Šír
Transformer models have continuously expanded into all machine learning domains convertible to the underlying sequence-to-sequence representation, including tabular data. However,…
Deep Learning for Generalised Planning with Background Knowledge
Dillon Z. Chen, Rostislav Horčík, Gustav Šír
Automated planning is a form of declarative problem solving which has recently drawn attention from the machine learning (ML) community. ML has been applied to planning either as a…
Beating the market with a bad predictive model
Ondřej Hubáček, Gustav Šír
It is a common misconception that in order to make consistent profits as a trader, one needs to posses some extra information leading to an asset value estimation more accurate tha…