1 citations · 2 across the 10 of their papers we have counts for
6 papers · 1 filter
General Machine Learning: Theory for Learning Under Variable Regimes
Aomar Osmani
We study learning under regime variation, where the learner, its memory state, and the evaluative conditions may evolve over time. This paper is a foundational and structural contr…
Meta-RL with Shared Representations Enables Fast Adaptation in Energy Systems
Théo Zangato, Aomar Osmani, Pegah Alizadeh
Meta-Reinforcement Learning addresses the critical limitations of conventional Reinforcement Learning in multi-task and non-stationary environments by enabling fast policy adaptati…
Data-Driven Policy Mapping for Safe RL-based Energy Management Systems
Theo Zangato, Aomar Osmani, Pegah Alizadeh
Increasing global energy demand and renewable integration complexity have placed buildings at the center of sustainable energy management. We present a three-step reinforcement lea…
On the Necessity of Metalearning: Learning Suitable Parameterizations for Learning Processes
Massinissa Hamidi, Aomar Osmani
In this paper we will discuss metalearning and how we can go beyond the current classical learning paradigm. We will first address the importance of inductive biases in the learnin…
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition
Aomar Osmani, Massinissa Hamidi, Pegah Alizadeh
In multi-class classification tasks, like human activity recognition, it is often assumed that classes are separable. In real applications, this assumption becomes strong and gener…
Description of Structural Biases and Associated Data in Sensor-Rich Environments
Massinissa Hamidi, Aomar Osmani
In this article, we study activity recognition in the context of sensor-rich environments. We address, in particular, the problem of inductive biases and their impact on the data c…