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

Katrina Ligett, Seth Neel, Aaron Roth +2

Bounded-Loss Private Prediction Markets

Rafael Frongillo, Bo Waggoner

Decentralized & Collaborative AI on Blockchain

Justin D. Harris, Bo Waggoner

Local Differential Privacy for Evolving Data

Matthew Joseph, Aaron Roth, Jonathan Ullman +1

An Axiomatic Study of Scoring Rule Markets

Rafael Frongillo, Bo Waggoner

$\ell_p$ Testing and Learning of Discrete Distributions

Bo Waggoner

Designing Markets for Daily Deals

Yang Cai, Mohammad Mahdian, Aranyak Mehta +1

Strategic Classification from Revealed Preferences

Jinshuo Dong, Aaron Roth, Zachary Schutzman +2

Multi-Observation Elicitation

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

Toward a Characterization of Loss Functions for Distribution Learning

Nika Haghtalab, Cameron Musco, Bo Waggoner

The paper investigates loss functions for learning probability distributions over large discrete domains, proposes desirable criteria for such losses, and shows that while no loss…

Multi-Observation Regression

Rafael Frongillo, Nishant A. Mehta, Tom Morgan +1

A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem

Sampath Kannan, Jamie Morgenstern, Aaron Roth +2

Informational Substitutes

Yiling Chen, Bo Waggoner

Low-Cost Learning via Active Data Procurement

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