2 citations · 6 across the 4 of their papers we have counts for
4 papers · 1 filter
Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning
Katherine Metcalf, Miguel Sarabia, Barry-John Theobald
Preference-based reinforcement learning (RL) algorithms help avoid the pitfalls of hand-crafted reward functions by distilling them from human preference feedback, but they remain…
Symbol Guided Hindsight Priors for Reward Learning from Human Preferences
Mudit Verma, Katherine Metcalf
Specifying rewards for reinforcement learned (RL) agents is challenging. Preference-based RL (PbRL) mitigates these challenges by inferring a reward from feedback over sets of traj…
FedEmbed: Personalized Private Federated Learning
Andrew Silva, Katherine Metcalf, Nicholas Apostoloff +1
Federated learning enables the deployment of machine learning to problems for which centralized data collection is impractical. Adding differential privacy guarantees bounds on pri…
Learning Sharing Behaviors with Arbitrary Numbers of Agents
Katherine Metcalf, Barry-John Theobald, Nicholas Apostoloff
We propose a method for modeling and learning turn-taking behaviors for accessing a shared resource. We model the individual behavior for each agent in an interaction and then use…