3 citations · 3 across the 14 of their papers we have counts for
8 papers · 1 filter
CalTwin: Towards Calibrated, Shift-Robust Medical World Models via Fisher-Information Regularisation
Behraj Khan, Shabir Ahmad, Syed Ahmad Chan Bukhari +1
Medical world models aim to learn a latent state of patient or organ physiology and a transition function that forecasts how that state evolves under interventions, supporting down…
PIcsC: Partitioning-Induced Covariate Shift Correction
Behraj Khan, Behroz Mirza, Syed Ahmad Chan Bukhari +1
Covariate shift across training-data partitions biases model selection and parameter estimation in cross-validation, lifelong learning, and federated learning. We propose \textit{P…
Technical note on Fisher Information for Robust Federated Cross-Validation
Behraj Khan, Tahir Qasim Syed
When training data are fragmented across batches or federated-learned across different geographic locations, trained models manifest performance degradation. That degradation partl…
Technical note on Sequential Test-Time Adaptation via Martingale-Driven Fisher Prompting
Behraj Khan, Tahir Qasim Syed
We present a theoretical framework for M-FISHER, a method for sequential distribution shift detection and stable adaptation in streaming data. For detection, we construct an expone…
Adapting to Fragmented and Evolving Data: A Fisher Information Perspective
Behraj Khan, Tahir Qasim Syed, Nouman Muhammad Durrani
Modern machine learning systems operating in dynamic environments often face \textit{sequential covariate shift} (SCS), where input distributions evolve over time while the conditi…
Efficient Learning Under Density Shift in Incremental Settings Using Cramér-Rao-Based Regularization
Behraj Khan, Behroz Mirza, Nouman Durrani +1
The continuous surge in data volume and velocity is often dealt with using data orchestration and distributed processing approaches, abstracting away the machine learning challenge…