Demystifying Low-Rank Knowledge Distillation in Large Language Models: Convergence, Generalization, and Information-Theoretic Guarantees

Collection of recent machine-learning research preprints (arXiv stat/ML) covering theoretical and algorithmic advances across language model compression, Bayesian inference, privacy-preserving RLHF, PAC-Bayesian analysis, active learning with Rashomon ensembles, variational inference with vine copulas, post-selection model evaluation, tensor-network Fourier methods for compressed aggregate distributions, weighted conformal anomaly detection, contextual graph matching with correlated features, and climate-model emulation with ML. Highlights include a rigorous theoretical framework for low-rank/