2 citations · 3 across the 2 of their papers we have counts for
6 papers
An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
Taylor W. Killian, Haoran Zhang, Jayakumar Subramanian +2
Reinforcement Learning (RL) has recently been applied to sequential estimation and prediction problems identifying and developing hypothetical treatment strategies for septic patie…
Multiple Sclerosis Severity Classification From Clinical Text
Alister D Costa, Stefan Denkovski, Michal Malyska +5
Multiple Sclerosis (MS) is a chronic, inflammatory and degenerative neurological disease, which is monitored by a specialist using the Expanded Disability Status Scale (EDSS) and r…
Kernelized Capsule Networks
Taylor Killian, Justin Goodwin, Olivia Brown +1
Capsule Networks attempt to represent patterns in images in a way that preserves hierarchical spatial relationships. Additionally, research has demonstrated that these techniques m…
Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning
Andrew Silva, Taylor Killian, Ivan Dario Jimenez Rodriguez +2
Decision trees are ubiquitous in machine learning for their ease of use and interpretability. Yet, these models are not typically employed in reinforcement learning as they cannot…
Learning Robust Representations for Automatic Target Recognition
Justin A. Goodwin, Olivia M. Brown, Taylor W. Killian +1
Radio frequency (RF) sensors are used alongside other sensing modalities to provide rich representations of the world. Given the high variability of complex-valued target responses…
Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes
Taylor Killian, Samuel Daulton, George Konidaris +1
We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings…