Author

S. Bukhari

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Preprint Sep 2026

Technical note on: Zero-Training Feature-Space Alignment via Information Geometry

Deep vision models often degrade under distribution shift. Test-time adaptation can improve robustness but typically requires iterative optimization, hyperparameter tuning, and multiple forward-backward passes. We propose Zero-Training Fisher Geometry Alignment (ZFGA), a closed-form method that improves robustness unde...

Behraj Khan, T. Syed, Syed Ahmad Chan Bukhari · 0 citations

Jul 2026

PIcsC: Partitioning-Induced Covariate Shift Correction

The results demonstrate that Fisher information provides an effective and unified mechanism for mitigating partition-induced covariate shift across both centralized and distributed learning.

Behraj Khan, Behroz Mirza, Syed Ahmad Chan Bukhari et al. · arXiv.org · 0 citations

Jul 2026

CalTwin: Towards Calibrated, Shift-Robust Medical World Models via Fisher-Information Regularisation

The combined objective is derived, which proof steps transfer from the classification setting without modification and which require adaptation, and which require adaptation on the PhysioNet 2019 Sepsis Challenge, treating the two hospital systems as sequential training fragments and the unseen system as an out-of-dist...

Behraj Khan, Shabir Ahmad, S. Bukhari et al. · arXiv.org · 1 citation