7 citations · 12 across the 5 of their papers we have counts for
6 papers
Task-Guided IRL in POMDPs that Scales
Franck Djeumou, Christian Ellis, Murat Cubuktepe +2
In inverse reinforcement learning (IRL), a learning agent infers a reward function encoding the underlying task using demonstrations from experts. However, many existing IRL techni…
Large Scale Radio Frequency Signal Classification
Luke Boegner, Manbir Gulati, Garrett Vanhoy +5
Existing datasets used to train deep learning models for narrowband radio frequency (RF) signal classification lack enough diversity in signal types and channel impairments to suff…
Risk Averse Bayesian Reward Learning for Autonomous Navigation from Human Demonstration
Christian Ellis, Maggie Wigness, John G. Rogers +2
Traditional imitation learning provides a set of methods and algorithms to learn a reward function or policy from expert demonstrations. Learning from demonstration has been shown…
Task-Guided Inverse Reinforcement Learning Under Partial Information
Franck Djeumou, Murat Cubuktepe, Craig Lennon +1
We study the problem of inverse reinforcement learning (IRL), where the learning agent recovers a reward function using expert demonstrations. Most of the existing IRL techniques m…
Evaluating NHL Goalies, Skaters, and Teams Using Weighted Shots
Brian Macdonald, Craig Lennon, Rodney Sturdivant
In this paper, we develop a logistic regression model to estimate the probability that a particular shot in an NHL game will result in a goal, and use the results to evaluate the p…
On the locality of the Prüfer code
Craig Lennon
The Prüfer code is a bijection between trees on the vertex set and strings on the set of length (Prüfer strings of order ). In this paper we examine the `local…