Quality Diversity for Reliable Data Driven Time-Use Optimization

A collection of new arXiv statistical machine learning papers covering uncertainty-aware optimization, causal inference, deep mixed-effects models, missing-data regression trees, localized conformal prediction, early-stopped gradient descent, reinforcement learning on persistence diagrams, learning under adversarial sample insertions, and scalable VARMA estimation. The material is research-focused and does not describe cybersecurity vulnerabilities, exploits, malware, or security incidents.