23 papers · 1 filter
Calibration-Preserving Pruning: Compression as a Reliability Contract
Ibne Farabi Shihab, Adria Binte Habib, Anuj Sharma
Split conformal prediction, not the pruning rule, supplies finite-sample marginal coverage once a pruned model is fixed independently of the conformal calibration split. We study t…
CODS: Iterative Bellman-Residual Data Selection for Reusable Offline Reinforcement Learning
Ibne Farabi Shihab, Sanjeda Akter, Abu Sa-Adat Mohamed Moon-Im Al Ahsan +2
Offline reinforcement learning repeatedly trains policies from a fixed transition pool, making redundant data costly across seeds and hyperparameters, while naive subsampling can r…
Discrepancy-Rounded Fair Bandits with Static and Time-Varying Exposure Floors
Ibne Farabi Shihab, Joyanta Jyoti Mondal, Anuj Sharma
Minimum-exposure constraints arise in recommendation, content curation, and regulated allocation when each provider, arm, or group must receive guaranteed exposure inside a period…
Graph Dimensionality Reduction for Contextual Bandits: Structure-Specific Regret Bounds under Approximate Smoothness and Noisy Eigenspaces
Joyanta Jyoti Mondal, Ibne Farabi Shihab, Anuj Sharma
Contextual bandits with graph-structured arms arise in recommendation, citation retrieval, and social advertising, where arms connected on a graph tend to share reward signal. Stan…
Coverage-Based Calibration for Post-Training Quantization via Weighted Set Cover over Outlier Channels
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Post-Training Quantization (PTQ) compresses large language models to low bit-widths using a small calibration set, and its quality depends strongly on which samples are chosen. We…
Continual Calibration: Coverage Can Collapse Before Accuracy in Lifelong LLM Fine-Tuning
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Continual learning for large language models is typically evaluated through accuracy retention under sequential fine-tuning. We argue that this perspective is incomplete, because u…