26 citations · 33 across the 9 of their papers we have counts for
11 papers
TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners
Mostafa ElAraby, Samer B. Nashed, Liam Paull
The primary challenge of continual learning (CL) systems is to learn new tasks while remaining performant on previously learned tasks. A similarly important though less well-studie…
Perpetua: Multi-Hypothesis Persistence Modeling for Semi-Static Environments
Miguel Saavedra-Ruiz, Samer B. Nashed, Charlie Gauthier +1
Many robotic systems require extended deployments in complex, dynamic environments. In such deployments, parts of the environment may change between subsequent robot observations.…
Safety Representations for Safer Policy Learning
Kaustubh Mani, Vincent Mai, Charlie Gauthier +3
Reinforcement learning algorithms typically necessitate extensive exploration of the state space to find optimal policies. However, in safety-critical applications, the risks assoc…
RL: Boosting Meta Reinforcement Learning via RL inside RL
Abhinav Bhatia, Samer B. Nashed, Shlomo Zilberstein
Meta reinforcement learning (Meta-RL) methods such as RL have emerged as promising approaches for learning data-efficient RL algorithms tailored to a given task distribution. H…
Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities
Samer B. Nashed, Justin Svegliato, Su Lin Blodgett
As automated decision making and decision assistance systems become common in everyday life, research on the prevention or mitigation of potential harms that arise from decisions m…
Causal Explanations for Sequential Decision Making Under Uncertainty
Samer B. Nashed, Saaduddin Mahmud, Claudia V. Goldman +1
We introduce a novel framework for causal explanations of stochastic, sequential decision-making systems built on the well-studied structural causal model paradigm for causal reaso…