1 citations · 1 across the 8 of their papers we have counts for
10 papers
Graph World Models for Constrained Epidemic Policy Planning
Yiqi Su, Rashed Shelim, Lingyi Wang +2
Epidemic policy planning often requires coordination between geographical regions, taking into account mobility-driven spillovers and how to make use of limited resources. Existing…
MetaMind: General and Cognitive World Models in Multi-Agent Systems by Meta-Theory of Mind
Lingyi Wang, Rashed Shelim, Walid Saad +1
A major challenge for world models in multi-agent systems is to understand interdependent agent dynamics, predict interactive multi-agent trajectories, and plan over long horizons…
Dual-Mind World Models: A General Framework for Learning in Dynamic Wireless Networks
Lingyi Wang, Rashed Shelim, Walid Saad +1
Despite the popularity of reinforcement learning (RL) in wireless networks, existing approaches that rely on model-free RL (MFRL) and model-based RL (MBRL) are data inefficient and…
A Theoretically-Grounded Codebook for Digital Semantic Communications
Lingyi Wang, Rashed Shelim, Walid Saad +1
The use of a learnable codebook provides an efficient way for semantic communications to map vector-based high-dimensional semantic features onto discrete symbol representations re…
Dynamic Strategy Adaptation in Multi-Agent Environments with Large Language Models
Shaurya Mallampati, Rashed Shelim, Walid Saad +1
Large language models (LLMs) demonstrate strong reasoning abilities across mathematical, strategic, and linguistic tasks, yet little is known about how well they reason in dynamic,…
When can isotropy help adapt LLMs' next word prediction to numerical domains?
Rashed Shelim, Shengzhe Xu, Walid Saad +1
Vector representations of contextual embeddings learned by pre-trained large language models (LLMs) are effective in various downstream tasks in numerical domains such as time seri…
World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks
Lingyi Wang, Rashed Shelim, Walid Saad +1
Traditional reinforcement learning (RL)-based learning approaches for wireless networks rely on expensive trial-and-error mechanisms and real-time feedback based on extensive envir…
DMWM: Dual-Mind World Model with Long-Term Imagination
Lingyi Wang, Rashed Shelim, Walid Saad +1
Imagination in world models is crucial for enabling agents to learn long-horizon policy in a sample-efficient manner. Existing recurrent state-space model (RSSM)-based world models…
Fast Geometric Learning of MIMO Signal Detection over Grassmannian Manifolds
Rashed Shelim, Walid Saad, Naren Ramakrishnan
Domain or statistical distribution shifts are a key staple of the wireless communication channel, because of the dynamics of the environment. Deep learning (DL) models for detectin…
A Fast Graph Kernel Based Classification Method for Wireless Link Scheduling on Riemannian Manifold
Rashed Shelim, Ahmed S. Ibrahim
In this paper, we propose a novel graph kernel method for the wireless link scheduling problem in device-to-device (D2D) networks on Riemannian manifold. The link scheduling proble…