2 citations · 2 across the 4 of their papers we have counts for
6 papers
Personalized Recommendations Without Inducing Congestion: Mitigating Disparities in the NYC High School Match
Erica Chiang, Kenny Peng, Rebecca Lichtenstein +7
Algorithmic recommendations can help participants navigate large matching markets. For example, recommendations for school and college choices may reduce information frictions and…
A Testable Theory of Atomic Features
Kenny Peng, Jon Kleinberg, Nikhil Garg
We develop and test a theory of language model representations in which there exist atomic features. Our main theoretical insight is that in such a model, sparse dictionaries (e.g.…
How Many Features Can a Language Model Store Under the Linear Representation Hypothesis?
Nikhil Garg, Jon Kleinberg, Kenny Peng
We introduce a mathematical framework for the linear representation hypothesis (LRH), which asserts that intermediate layers of language models store features linearly. We separate…
Position: Use Sparse Autoencoders to Discover Unknowns
Kenny Peng, Rajiv Movva, Jon Kleinberg +2
While sparse autoencoders (SAEs) have generated significant excitement, a series of negative results have added to skepticism about their usefulness. Here, we establish a conceptua…
Sparse Autoencoders for Hypothesis Generation
Rajiv Movva, Kenny Peng, Nikhil Garg +2
We describe HypotheSAEs, a general method to hypothesize interpretable relationships between text data (e.g., headlines) and a target variable (e.g., clicks). HypotheSAEs has three…
A No Free Lunch Theorem for Human-AI Collaboration
Kenny Peng, Nikhil Garg, Jon Kleinberg
The gold standard in human-AI collaboration is complementarity -- when combined performance exceeds both the human and algorithm alone. We investigate this challenge in binary clas…