Statistics & Optimization for Trustworthy AI

Announcements

• I am recruiting a postdoctoral scholar starting in early 2025:
  • Please fill the brief Google form to apply: https://forms.gle/q4qcWg3JYXJ42F9B6
  • Topics of interest:
    • methods and theory for LLMs including reasoning and test-time compute
    • foundation models for time-series and tabular data, next-gen AutoML
• My student Yingcong Li will be in the academic job market

Recent news

• New award from Amazon Research on Foundation Model Development
• 2 papers are accepted to AAAI 2025
  • On the Power of Convolution Augmented Transformer
  • TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data
• We are presenting 4 papers at NeurIPS 2024
  • Selective Attention: Enhancing Transformer through Principled Context Control
  • Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning
  • Fine-grained Analysis of In-context Linear Estimation
  • CONTRAST: Continual Multi-source Adaptation to Dynamic Distributions
• Congrats to Mingchen on his graduation and joining Meta as a Research Scientist!
• I will serve as a Senior Area Chair for NeurIPS 2024.
• Congrats to our 2023 interns who will pursue their PhD studies in UC Berkeley, Harvard, and UIUC!
• Two papers at ICML 2024: Self-Attention <=> Markov Models and Can Mamba Learn How to Learn?
• New course on Foundations of Large Language Models: syllabus (including Piazza and logistics)
• New awards from NSF and ONR: We kickstarted two exciting projects to advance the theoretical and algorithmic foundations of LLMs, transformers, and their compositional learning capabilities.
• Two papers at AISTATS 2024
  • “Mechanics of Next Token Prediction with Self-Attention”, Y. Li, Y. Huang, M.E. Ildiz, A.S. Rawat, S.O.
  • “Inverse Scaling and Emergence in Multitask Representations“, M.E. Ildiz, Z. Zhao, S.O.
• Two papers at AAAI 2024 and one paper at WACV 2024
  • Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective
  • Effective Restoration of Source Knowledge in Continual Test Time Adaptation
• Invited talks at USC, INFORMS, Yale, Google NYC, and Harvard on our works on transformer theory
• Two papers at NeurIPS 2023
  • Max-Margin Token Selection in Attention Mechanism, spotlight paper!
  • Dissecting Chain-of-Thought: A Study on Compositional In-Context Learning of MLPs
• Grateful for the Adobe Data Science Research award!
• Our new works develop the optimization foundations of Transformers via SVM connection
  • Transformers as Support Vector Machines => Twitter thread
  • Max-Margin Token Selection in Attention Mechanism, NeurIPS’23
• Two papers at ICML 2023: Transformers as Algorithms and On the Role of Attention in Prompt-tuning
• Two papers at AAAI 2023: Provable Pathways and Long Horizon Bandits

We are grateful for our research sponsors