activity
20142024
most citedDifferential Privacy and Machine Learning: a Survey and Review

193 citations · 332 across the 28 of their papers we have counts for

collaborators

22 papers

cs.CV2023

MoCo-Transfer: Investigating out-of-distribution contrastive learning for limited-data domains

Yuwen Chen, Helen Zhou, Zachary C. Lipton

Medical imaging data is often siloed within hospitals, limiting the amount of data available for specialized model development. With limited in-domain data, one might hope to lever…

cs.CV2023

Reading Between the Mud: A Challenging Motorcycle Racer Number Dataset

Jacob Tyo, Youngseog Chung, Motolani Olarinre +1

This paper introduces the off-road motorcycle Racer number Dataset (RnD), a new challenging dataset for optical character recognition (OCR) research. RnD contains 2,411 images from…

cs.CV2023

MUDD: A New Re-Identification Dataset with Efficient Annotation for Off-Road Racers in Extreme Conditions

Jacob Tyo, Motolani Olarinre, Youngseog Chung +1

Re-identifying individuals in unconstrained environments remains an open challenge in computer vision. We introduce the Muddy Racer re-IDentification Dataset (MUDD), the first larg…

stat.ME20232 cited

Estimating the Likelihood of Arrest from Police Records in Presence of Unreported Crimes

Riccardo Fogliato, Arun Kumar Kuchibhotla, Zachary Lipton +3

Many important policy decisions concerning policing hinge on our understanding of how likely various criminal offenses are to result in arrests. Since many crimes are never reporte…

cs.CL2023

Goodhart's Law Applies to NLP's Explanation Benchmarks

Jennifer Hsia, Danish Pruthi, Aarti Singh +1

Despite the rising popularity of saliency-based explanations, the research community remains at an impasse, facing doubts concerning their purpose, efficacy, and tendency to contra…

cs.LG20231 cited

Can Neural Network Memorization Be Localized?

Pratyush Maini, Michael C. Mozer, Hanie Sedghi +3

Recent efforts at explaining the interplay of memorization and generalization in deep overparametrized networks have posited that neural networks "hard" example…