1 citations · 3 across the 9 of their papers we have counts for
11 papers
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…
Incentives shape how humans co-create with generative AI
Nathanael Jo, Manish Raghavan
Generative AI is quickly becoming an integral part of people's everyday workflows. Early evidence has shown that while generative AI can increase individual-level productivity, it…
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…
The Impossibility of Inverse Permutation Learning in Transformer Models
Rohan Alur, Chris Hays, Manish Raghavan +1
In this technical note, we study the problem of inverse permutation learning in decoder-only transformers. Given a permutation and a string to which that permutation has been appli…
Double Machine Learning for Causal Inference under Shared-State Interference
Chris Hays, Manish Raghavan
Researchers and practitioners often wish to measure treatment effects in settings where units interact via markets and recommendation systems. In these settings, units are affected…
Homogeneous Algorithms Can Reduce Competition in Personalized Pricing
Nathanael Jo, Kathleen Creel, Ashia Wilson +1
Firms' algorithm development practices are often homogeneous. Whether firms train algorithms on similar data, aim at similar benchmarks, or rely on similar pre-trained models, the…
Evaluating multiple models using labeled and unlabeled data
Divya Shanmugam, Shuvom Sadhuka, Manish Raghavan +3
It remains difficult to evaluate machine learning classifiers in the absence of a large, labeled dataset. While labeled data can be prohibitively expensive or impossible to obtain,…
Competition and Diversity in Generative AI
Manish Raghavan
Recent evidence, both in the lab and in the wild, suggests that the use of generative artificial intelligence reduces the diversity of content produced. The use of the same or simi…
Integrating Expert Judgment and Algorithmic Decision Making: An Indistinguishability Framework
Rohan Alur, Loren Laine, Darrick K. Li +3
We introduce a novel framework for human-AI collaboration in prediction and decision tasks. Our approach leverages human judgment to distinguish inputs which are algorithmically in…
Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models
Vinith M. Suriyakumar, Rohan Alur, Ayush Sekhari +2
Text-to-image diffusion models rely on massive, web-scale datasets. Training them from scratch is computationally expensive, and as a result, developers often prefer to make increm…
Human Expertise in Algorithmic Prediction
Rohan Alur, Manish Raghavan, Devavrat Shah
We introduce a novel framework for incorporating human expertise into algorithmic predictions. Our approach leverages human judgment to distinguish inputs which are algorithmically…