AI Foundational Optimizations
Overview
Focuses on improving the efficiency, scalability, and reliability of core AI models and algorithms. By optimizing training, architectures, and computation, this subgroup enables robust, high-performance AI adaptable to complex, data-driven tasks.
Team
Collaborators
Related publications
AI Foundational Optimizations2026
Rapid disaster damage assessment using deep adversarial sliced Wasserstein domain adaptation
Fatma AlNaimi, Abdulaziz Al-Homaid, Ferda Ofli, Abdelkader Baggag — Neural Computing and Applications (2026)
AI Foundational Optimizations2026
Particles Don’t Care About Z: Towards Scaling Entropy Estimation of Unnormalized Densities
Safa Messaoud, Skander Charni, Elaa Bouazza, Ali Pourghasemi, Halima Bensmail — ICML (2026)
AI Foundational Optimizations2026
ISilDR: Isometric Seriation-based Dimensionality Reduction for Visual Cluster Analysis
Rene Cutura, Sophie Sadler, Quynh Quang Ngo, Michaël Aupetit, Michael Sedlmair — IEEE Transactions on Visualization and Computer Graphics (2026)
AI Foundational Optimizations2025
Uncertainty-Aware LLMs Fail to Flag Misleading Contexts
Tianyi Zhou, Johanne Medina, Sanjay Chawla — NeurIPS 2025 – Reliable ML Workshop (2025)
AI Foundational Optimizations2025
Explaining the role of Intrinsic Dimensionality in Adversarial Training
Enes Altinisik, Safa Messaoud, Husrev Taha Sencar, Hassan Sajjad, Sanjay Chawla — ICML (2025)
1–5 of 16 publications