Statistics & Optimization for Trustworthy AI
Our Research
We develop principled and empirically-impactful AI/ML methods
- mathematical foundations of transformers and attention
- trustworthy and efficient language models, LLM systems
- reinforcement learning, control, LLMs as interactive agents
- core optimization and statistical learning theory
News
- I will serve as a Senior Area Chair for NeurIPS 2024.
- New course: Foundations of Large Language Models.
- Link to syllabus (including Piazza and logistics)
- New paper: From Self-Attention to Markov Models, M.E. Ildiz, Y. Huang, Y. Li, A.S. Rawat, S.O.
- Two papers are accepted to AISTATS 2024 (papers upcoming)
- “Mechanics of Next Token Prediction with Self-Attention”, Y. Li, Y. Huang, M.E. Ildiz, A.S. Rawat, S.O.
- “Risk and Emergence of Individual Tasks in Multitask Representations”, M.E. Ildiz, Z. Zhao, S.O.
- Two papers are accepted to AAAI 2024 and one paper is accepted to WACV 2024
- Invited talks at USC, INFORMS, Yale, Google NYC, and Harvard on our works on transformer theory
- Transformers as SVMs and FedYolo will appear in NeurIPS workshops
- Two papers are accepted to NeurIPS 2023
- Grateful for the Adobe Data Science Research award!
- Our new works develop the optimization foundations of Transformers via SVM connection
- Two papers appeared at ICML 2023
- Two papers appeared at AAAI 2023: Provable Pathways and Long Horizon Bandits
- Papers to appear at AutoML 2023 and ICASSP 2023.
- One paper is accepted to L4DC 2023 as oral presentation.