5 papers
Measurements Number Scaling in the Quantum Approximate Optimization Algorithm for MaxCut: A Statistical Analysis
Inbar Chefer, Uri Shaham, Adi Makmal
We provide a statistical analysis of the measurement (shot) requirements of the quantum approximate optimization algorithm (QAOA) for the MaxCut problem. We derive sufficient condi…
Convexified Message-Passing Graph Neural Networks
Saar Cohen, Noa Agmon, Uri Shaham
Graph Neural Networks (GNNs) are key tools for graph representation learning, demonstrating strong results across diverse prediction tasks. In this paper, we present Convexified Me…
P-CAFE: Personalized Cost-Aware Incremental Feature Selection For Electronic Health Records
Naama Kashani, Mira Cohen, Uri Shaham
Electronic Health Records (EHR) have revolutionized healthcare by digitizing patient data, improving accessibility, and streamlining clinical workflows. However, extracting meaning…
Enhancing VICReg: Random-Walk Pairing for Improved Generalization and Better Global Semantics Capturing
Idan Simai, Ronen Talmon, Uri Shaham
In this paper, we argue that viewing VICReg-a popular self-supervised learning (SSL) method--through the lens of spectral embedding reveals a potential source of sub-optimality: it…
SPARC: Spectral Architectures Tackling the Cold-Start Problem in Graph Learning
Yahel Jacobs, Reut Dayan, Uri Shaham
Graphs play a central role in modeling complex relationships in data, yet most graph learning methods falter when faced with cold-start nodes--new nodes lacking initial connections…