2 citations · 2 across the 4 of their papers we have counts for
6 papers · 1 filter
multivariateGPT: a decoder-only transformer for multivariate categorical and numeric data
Andrew J. Loza, Jun Yup Kim, Shangzheng Song +4
Real-world processes often generate data that are a mix of categorical and numeric values that are recorded at irregular and informative intervals. Discrete token-based approaches…
LAMP: Extracting Local Decision Surfaces From Large Language Models
Ryan Chen, Youngmin Ko, Zeyu Zhang +5
We introduce LAMP (Local Attribution Mapping Probe), a method that shines light onto a black-box language model's decision surface and studies how reliably a model maps its stated…
Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium
Amin Adibi, Xu Cao, Zongliang Ji +39
The fourth Machine Learning for Health (ML4H) symposium was held in person on December 15th and 16th, 2024, in the traditional, ancestral, and unceded territories of the Musqueam,…
Integrating Expert Judgment and Algorithmic Decision Making: An Indistinguishability Framework
Rohan Alur, Loren Laine, Darrick K. Li +3
We introduce a novel framework for human-AI collaboration in prediction and decision tasks. Our approach leverages human judgment to distinguish inputs which are algorithmically in…
Trajectory Flow Matching with Applications to Clinical Time Series Modeling
Xi Zhang, Yuan Pu, Yuki Kawamura +4
Modeling stochastic and irregularly sampled time series is a challenging problem found in a wide range of applications, especially in medicine. Neural stochastic differential equat…
Making Logic Learnable With Neural Networks
Tobias Brudermueller, Dennis L. Shung, Adrian J. Stanley +2
While neural networks are good at learning unspecified functions from training samples, they cannot be directly implemented in hardware and are often not interpretable or formally…