24 citations · 61 across the 17 of their papers we have counts for
11 papers · 1 filter
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…
WavesFM: Hierarchical Representation Learning for Longitudinal Wearable Sensor Waveforms
Peng Cao, Zhijian Yang, Tennison Liu +17
Wearable sensors enable the continuous acquisition of high-resolution physiological waveforms, such as photoplethysmography and accelerometry, under free-living conditions. However…
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…
Autoformulation of Mathematical Optimization Models Using LLMs
Nicolás Astorga, Tennison Liu, Yuanzhang Xiao +1
Mathematical optimization is fundamental to decision-making across diverse domains, from operations research to healthcare. Yet, translating real-world problems into optimization m…
Automatically Learning Hybrid Digital Twins of Dynamical Systems
Samuel Holt, Tennison Liu, Mihaela van der Schaar
Digital Twins (DTs) are computational models that simulate the states and temporal dynamics of real-world systems, playing a crucial role in prediction, understanding, and decision…
Unveiling the Power of Sparse Neural Networks for Feature Selection
Zahra Atashgahi, Tennison Liu, Mykola Pechenizkiy +3
Sparse Neural Networks (SNNs) have emerged as powerful tools for efficient feature selection. Leveraging the dynamic sparse training (DST) algorithms within SNNs has demonstrated p…
Large Language Models to Enhance Bayesian Optimization
Tennison Liu, Nicolás Astorga, Nabeel Seedat +1
Bayesian optimization (BO) is a powerful approach for optimizing complex and expensive-to-evaluate black-box functions. Its importance is underscored in many applications, notably…
Learning Representations without Compositional Assumptions
Tennison Liu, Jeroen Berrevoets, Zhaozhi Qian +1
This paper addresses unsupervised representation learning on tabular data containing multiple views generated by distinct sources of measurement. Traditional methods, which tackle…
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
Alan Jeffares, Tennison Liu, Jonathan Crabbé +2
Despite their success with unstructured data, deep neural networks are not yet a panacea for structured tabular data. In the tabular domain, their efficiency crucially relies on va…
Joint Training of Deep Ensembles Fails Due to Learner Collusion
Alan Jeffares, Tennison Liu, Jonathan Crabbé +1
Ensembles of machine learning models have been well established as a powerful method of improving performance over a single model. Traditionally, ensembling algorithms train their…
Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes
Tennison Liu, Alex J. Chan, Boris van Breugel +1
It is important to guarantee that machine learning algorithms deployed in the real world do not result in unfairness or unintended social consequences. Fair ML has largely focused…