most citedAccuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM

32 citations · 52 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG20176 cited

Strategic Classification from Revealed Preferences

Jinshuo Dong, Aaron Roth, Zachary Schutzman +2

We study an online linear classification problem, in which the data is generated by strategic agents who manipulate their features in an effort to change the classification outcome…

cs.GT20172 cited

An Axiomatic Study of Scoring Rule Markets

Rafael Frongillo, Bo Waggoner

Prediction markets are well-studied in the case where predictions are probabilities or expectations of future random variables. In 2008, Lambert, et al. proposed a generalization,…

cs.LG2017

Multi-Observation Elicitation

Sebastian Casalaina-Martin, Rafael Frongillo, Tom Morgan +1

We study loss functions that measure the accuracy of a prediction based on multiple data points simultaneously. To our knowledge, such loss functions have not been studied before i…

cs.LG201732 cited

Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM

Katrina Ligett, Seth Neel, Aaron Roth +2

Traditional approaches to differential privacy assume a fixed privacy requirement for a computation, and attempt to maximize the accuracy of the computation subject to the priv…

cs.GT2017

Informational Substitutes

Yiling Chen, Bo Waggoner

We propose definitions of substitutes and complements for pieces of information ("signals") in the context of a decision or optimization problem, with game-theoretic and algorithmi…

cs.GT201512 cited

Low-Cost Learning via Active Data Procurement

Jacob Abernethy, Yiling Chen, Chien-Ju Ho +1

We design mechanisms for online procurement of data held by strategic agents for machine learning tasks. The challenge is to use past data to actively price future data and give le…