The goal of this bootcamp is to equip statisticians with a working command of advanced AI tools, especially large language models (LLMs), deep learning, reinforcement learning, agents, and AI alignment. Specific topics going to be covered:
- AI alignment statistically framed: turn safety/helpfulness/non-toxicity into measurable quantities (hypothesis tests, risk bounds, selective prediction/abstention); practice red-teaming and auditing with error control.
- Deep learning through a statistical lens: generalization under shift, robustness, calibration and when bounds meaningfully predict deployment behavior.
- Large language models (LLMs)
- AI agents: decision loops with uncertainty and feedback; risk-aware policies, auditable logs, and monitoring.
- Causal reasoning with LLMs: distinguishing predictive vs. causal targets; sensitivity analyses; documenting assumptions for defensible decisions.
- Reinforcement learning: statistically principled exploration, off-policy evaluation, confidence sets, and safety/fairness implications.
Confirmed Speakers
Bang Liu (Université de Montréal)
Jiancong Xiao (University of Pennsylvania)
Marouane Idrissi (UQAM)
Yanxun Xu (Johns Hopkins University)
Amirhossein Kazemnejad (Mila).