4 citations · 12 across the 11 of their papers we have counts for
10 papers · 1 filter
SeqMaestro: From nucleotide sequences to biological hypotheses through interpretable machine learning
Evgeny S. Saveliev, Krzysztof Kacprzyk, Charlotte Capitanchik +7
Nucleotide sequence analysis is central to problems spanning regulatory genomics, evolutionary biology, and phenotype prediction. Classical bioinformatics methods extract interpret…
Interpretable DNA Sequence Classification via Dynamic Feature Generation in Decision Trees
Nicolas Huynh, Krzysztof Kacprzyk, Ryan Sheridan +2
The analysis of DNA sequences has become critical in numerous fields, from evolutionary biology to understanding gene regulation and disease mechanisms. While deep neural networks…
Beyond the ATE: Interpretable Modelling of Treatment Effects over Dose and Time
Julianna Piskorz, Krzysztof Kacprzyk, Harry Amad +1
The Average Treatment Effect (ATE) is a foundational metric in causal inference, widely used to assess intervention efficacy in randomized controlled trials (RCTs). However, in man…
No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs
Krzysztof Kacprzyk, Mihaela van der Schaar
Data-driven modeling of dynamical systems is a crucial area of machine learning. In many scenarios, a thorough understanding of the model's behavior becomes essential for practical…
Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments
Paulius Rauba, Nabeel Seedat, Krzysztof Kacprzyk +1
Real-world machine learning systems often encounter model performance degradation due to distributional shifts in the underlying data generating process (DGP). Existing approaches…
Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations
Krzysztof Kacprzyk, Mihaela van der Schaar
Symbolic regression has excelled in uncovering equations from physics, chemistry, biology, and related disciplines. However, its effectiveness becomes less certain when applied to…