4 citations · 12 across the 11 of their papers we have counts for
12 papers
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…
Revolutionizing Clinical Trials: A Manifesto for AI-Driven Transformation
Mihaela van der Schaar, Richard Peck, Eoin McKinney +15
This manifesto represents a collaborative vision forged by leaders in pharmaceuticals, consulting firms, clinical research, and AI. It outlines a roadmap for two AI technologies -…
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…
ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference
Krzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets +2
Inferring unbiased treatment effects has received widespread attention in the machine learning community. In recent years, our community has proposed numerous solutions in standard…
Causal Deep Learning
Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian +1
Causality has the potential to truly transform the way we solve a large number of real-world problems. Yet, so far, its potential largely remains to be unlocked as causality often…
Navigating causal deep learning
Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian +1
Causal deep learning (CDL) is a new and important research area in the larger field of machine learning. With CDL, researchers aim to structure and encode causal knowledge in the e…
D-CIPHER: Discovery of Closed-form Partial Differential Equations
Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar
Closed-form differential equations, including partial differential equations and higher-order ordinary differential equations, are one of the most important tools used by scientist…
Privacy-preserving Object Detection
Peiyang He, Charlie Griffin, Krzysztof Kacprzyk +4
Privacy considerations and bias in datasets are quickly becoming high-priority issues that the computer vision community needs to face. So far, little attention has been given to p…