1 citations · 2 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
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…
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…
DAGnosis: Localized Identification of Data Inconsistencies using Structures
Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat +3
Identification and appropriate handling of inconsistencies in data at deployment time is crucial to reliably use machine learning models. While recent data-centric methods are able…
Time Series Diffusion in the Frequency Domain
Jonathan Crabbé, Nicolas Huynh, Jan Stanczuk +1
Fourier analysis has been an instrumental tool in the development of signal processing. This leads us to wonder whether this framework could similarly benefit generative modelling.…
Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes
Nabeel Seedat, Nicolas Huynh, Boris van Breugel +1
Machine Learning (ML) in low-data settings remains an underappreciated yet crucial problem. Hence, data augmentation methods to increase the sample size of datasets needed for ML a…