24 citations · 61 across the 17 of their papers we have counts for
17 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…
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…
Hypothesis Hunting with Evolving Networks of Autonomous Scientific Agents
Tennison Liu, Silas Ruhrberg Estévez, David L. Bentley +1
Large-scale scientific datasets -- spanning health biobanks, cell atlases, Earth reanalyses, and more -- create opportunities for exploratory discovery unconstrained by specific re…
Truly Self-Improving Agents Require Intrinsic Metacognitive Learning
Tennison Liu, Mihaela van der Schaar
Self-improving agents aim to continuously acquire new capabilities with minimal supervision. However, current approaches face two key limitations: their self-improvement processes…
Agents Require Metacognitive and Strategic Reasoning to Succeed in the Coming Labor Markets
Simpson Zhang, Tennison Liu, Mihaela van der Schaar
Current labor markets are strongly affected by the economic forces of adverse selection, moral hazard, and reputation, each of which arises due to …
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…
Active Task Disambiguation with LLMs
Katarzyna Kobalczyk, Nicolas Astorga, Tennison Liu +1
Despite the impressive performance of large language models (LLMs) across various benchmarks, their ability to address ambiguously specified problems--frequent in real-world intera…
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…
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection
Daniel Jarrett, Bogdan Cebere, Tennison Liu +2
Consider the problem of imputing missing values in a dataset. One the one hand, conventional approaches using iterative imputation benefit from the simplicity and customizability o…
Epileptic Seizure Classification with Symmetric and Hybrid Bilinear Models
Tennison Liu, Nhan Duy Truong, Armin Nikpour +2
Epilepsy affects nearly 1% of the global population, of which two thirds can be treated by anti-epileptic drugs and a much lower percentage by surgery. Diagnostic procedures for ep…