3 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
GPU-GLMB: Assessing the Scalability of GPU-Accelerated Multi-Hypothesis Tracking
Pranav Balakrishnan, Sidisha Barik, Sean M. O'Rourke +1
Much recent research on multi-target tracking has focused on multi-hypothesis approaches leveraging random finite sets. Of particular interest are labeled random finite set methods…
ACE and Diverse Generalization via Selective Disagreement
Oliver Daniels, Stuart Armstrong, Alexandre Maranhão +3
Deep neural networks are notoriously sensitive to spurious correlations - where a model learns a shortcut that fails out-of-distribution. Existing work on spurious correlations has…
Enhancing Adaptive Behavioral Interventions with LLM Inference from Participant-Described States
Karine Karine, Benjamin M. Marlin
The use of reinforcement learning (RL) methods to support health behavior change via personalized and just-in-time adaptive interventions is of significant interest to health and b…
SigmaScheduling: Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventions
Asim H. Gazi, Bhanu Teja Gullapalli, Daiqi Gao +3
Timely decision making is critical to the effectiveness of mobile health (mHealth) interventions. At predefined timepoints called "decision points," intelligent mHealth systems suc…
Combining LLM decision and RL action selection to improve RL policy for adaptive interventions
Karine Karine, Benjamin M. Marlin
Reinforcement learning (RL) is increasingly being used in the healthcare domain, particularly for the development of personalized health adaptive interventions. Inspired by the suc…