76 citations · 308 across the 72 of their papers we have counts for
8 papers · 1 filter
Transfer Reinforcement Learning in Heterogeneous Action Spaces using Subgoal Mapping
Kavinayan P. Sivakumar, Yan Zhang, Zachary Bell +2
In this paper, we consider a transfer reinforcement learning problem involving agents with different action spaces. Specifically, for any new unseen task, the goal is to use a succ…
Inverse Reinforcement Learning from Non-Stationary Learning Agents
Kavinayan P. Sivakumar, Yi Shen, Zachary Bell +3
In this paper, we study an inverse reinforcement learning problem that involves learning the reward function of a learning agent using trajectory data collected while this agent is…
Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare
Xenia Konti, Hans Riess, Manos Giannopoulos +4
In this paper, we address the challenge of heterogeneous data distributions in cross-silo federated learning by introducing a novel algorithm, which we term Cross-silo Robust Clust…
Perception Stitching: Zero-Shot Perception Encoder Transfer for Visuomotor Robot Policies
Pingcheng Jian, Easop Lee, Zachary Bell +2
Vision-based imitation learning has shown promising capabilities of endowing robots with various motion skills given visual observation. However, current visuomotor policies fail t…
Risk-averse Learning with Non-Stationary Distributions
Siyi Wang, Zifan Wang, Xinlei Yi +3
Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time. In such cases, it is fa…
Learning of Nash Equilibria in Risk-Averse Games
Zifan Wang, Yi Shen, Michael M. Zavlanos +1
This paper considers risk-averse learning in convex games involving multiple agents that aim to minimize their individual risk of incurring significantly high costs. Specifically,…
Path Signatures and Graph Neural Networks for Slow Earthquake Analysis: Better Together?
Hans Riess, Manolis Veveakis, Michael M. Zavlanos
The path signature, having enjoyed recent success in the machine learning community, is a theoretically-driven method for engineering features from irregular paths. On the other ha…
Policy Evaluation in Distributional LQR (Extended Version)
Zifan Wang, Yulong Gao, Siyi Wang +3
Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random retu…