1 code implementation • 28 Jun 2023 • Abhinav Bhatia, Samer B. Nashed, Shlomo Zilberstein
Meta reinforcement learning (meta-RL) methods such as RL$^2$ have emerged as promising approaches for learning data-efficient RL algorithms tailored to a given task distribution.
no code implementations • 13 Jan 2023 • 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 made by these systems has proliferated.
no code implementations • 30 May 2022 • Samer B. Nashed, Saaduddin Mahmud, Claudia V. Goldman, Shlomo Zilberstein
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 reasoning.
no code implementations • 30 Jul 2020 • Samer B. Nashed
As mobile robot capabilities improve and deployment times increase, tools to analyze the growing volume of data are becoming necessary.
no code implementations • 4 Mar 2018 • Samer B. Nashed, David M. Ilstrup, Joydeep Biswas
We present the Variable Structure Multiple Hidden Markov Model (VSM-HMM) as a framework for localizing in the presence of topological uncertainty, and demonstrate its effectiveness on an AV where lane membership is modeled as a topological localization process.
1 code implementation • 23 Nov 2017 • Samer B. Nashed, Joydeep Biswas
Building large-scale, globally consistent maps is a challenging problem, made more difficult in environments with limited access, sparse features, or when using data collected by novice users.
Human-Computer Interaction Robotics