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…
Toward World Models for Epidemiology
Zeeshan Memon, Yiqi Su, Christo Kurisummoottil Thomas +3
World models have emerged as a unifying paradigm for learning latent dynamics, simulating counterfactual futures, and supporting planning under uncertainty. In this paper, we argue…
Adversarial Data Modeling in Epidemiology
Yiqi Su, Christo Kurisummoottil Thomas, Walid Saad +3
Epidemiological models increasingly rely on crowdsourced, self-reported behavioral data such as vaccination status, mask usage, and social distancing adherence. This data, however,…
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…