activity
20242026
collaborators

5 papers

cs.LG2026

CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment

Silas Ruhrberg Estévez, Nicolas Huynh, Tennison Liu +4

Inferring dynamics from population snapshots is a fundamental challenge in machine learning and biology. In scRNA-sequencing (scRNA-seq), destructive measurements preclude direct t…

cs.LG2026

Active Timepoint Selection for Learning Measure-Valued Trajectories

Nicolas Huynh, Mihaela van der Schaar

Inferring continuous probability paths from sparse snapshots is a fundamental challenge in domains like single-cell biology, where high-fidelity data acquisition is often destructi…

cs.LG2026

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…

cs.LG2025

Decision Tree Induction Through LLMs via Semantically-Aware Evolution

Tennison Liu, Nicolas Huynh, Mihaela van der Schaar

Decision trees are a crucial class of models offering robust predictive performance and inherent interpretability across various domains, including healthcare, finance, and logisti…

cs.LG2024

You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling

Nabeel Seedat, Nicolas Huynh, Fergus Imrie +1

Pseudo-labeling is a popular semi-supervised learning technique to leverage unlabeled data when labeled samples are scarce. The generation and selection of pseudo-labels heavily re…