Feature learning in kernel machines and applications to monitoring and steering LLMs
Mikhail Belkin, HDSI Endowed Chair Professor in AI, Halicioglu Data Science Institute, University of California San Diego
Abstract: Classical kernel machines are a powerful and theoretically grounded method for data analysis. However, they are not adaptive to low-dimensional "features" in the data.
In this talk I will discuss feature learning introducing Recursive Feature Machines—a powerful method designed for extracting relevant features from tabular data. I will discuss some of its interesting properties and, in particular, will show how this technique enables us to detect and precisely guide LLM behaviors toward almost any desired concept by manipulating a single fixed vector in the LLM activation space.
Cost: free
Kisa Kowal
(847) 491-3974
Email