5 papers
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…