2 citations · 4 across the 9 of their papers we have counts for
11 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.…
Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift
Kevin Ren, Manish Raghavan, Nikhil Garg
Deployed AI text detection approaches are supervised, relying on training-time access to labeled datasets of both human-written and AI-generated text. This approach is vulnerable t…
The Subjectivity of Monoculture
Nathanael Jo, Nikhil Garg, Manish Raghavan
Machine learning models---including large language models (LLMs)---are often said to exhibit monoculture, where outputs agree strikingly often. However, we present two experiments…
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…
Correlated Errors in Large Language Models
Elliot Kim, Avi Garg, Kenny Peng +1
Diversity in training data, architecture, and providers is assumed to mitigate homogeneity in LLMs. However, we lack empirical evidence on whether different LLMs differ meaningfull…
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…
Wisdom and Foolishness of Noisy Matching Markets
Kenny Peng, Nikhil Garg
We consider a many-to-one matching market where colleges share true preferences over students but make decisions using only independent noisy rankings. Each student has a true valu…
Monoculture in Matching Markets
Kenny Peng, Nikhil Garg
Algorithmic monoculture arises when many decision-makers rely on the same algorithm to evaluate applicants. An emerging body of work investigates possible harms of this kind of hom…