skip to main content

Explore Research Products in the NSF-PAR

  • Home
  • Search Results
  • Page 1 of 2

Search for: All records

Award ID contains: 1718549

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ( , Proceedings of the Ninth AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2021))
    Accepted Manuscript Full Text Available
  2. ; ( , Proceedings of the 22nd ACM Conference on Economics and Computation (EC 2021))
    Full Text Available 
  3. ; ; ( , Proc. of the Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020))
    Accepted Manuscript Full Text Available
  4. ; ; ( , Proc. of the Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020))
    Accepted Manuscript Full Text Available
  5. ; ; ( , ACM Conference on Economics and Computation)
    Full Text Available 
  6. ( , Proc. of ACM-SIAM Symposium on Discrete Algorithms (SODA), 2020.)
    Accepted Manuscript Full Text Available
  7. ; ( , Proceedings of the 2019 ACM Conference on Economics and Computation)
    Full Text Available 
  8. ; ( , Proceedings of the 2018 World Wide Web Conference)
    Full Text Available 
  9. ; ; ; ; ( , ACM Conference on Economics and Computation)
    We consider a data analyst's problem of purchasing data from strategic agents to compute an unbiased estimate of a statistic of interest. Agents incur private costs to reveal their data and the costs can be arbitrarily correlated with their data. Once revealed, data are verifiable. This paper focuses on linear unbiased estimators. We design an individually rational and incentive compatible mechanism that optimizes the worst-case mean-squared error of the estimation, where the worst-case is over the unknown correlation between costs and data, subject to a budget constraint in expectation. We characterize the form of the optimal mechanism in closed-form. We further extend our results to acquiring data for estimating a parameter in regression analysis, where private costs can correlate with the values of the dependent variable but not with the values of the independent variables. 
    more » « less
    Full Text Available 
  10. ; ; ; ( , Proceedings of the 2018 ACM Conference on Economics and Computation)
    This paper is part of an emerging line of work at the intersection of machine learning and mechanism design, which aims to avoid noise in training data by correctly aligning the incentives of data sources. Specifically, we focus on the ubiquitous problem of linear regression, where strategyproof mechanisms have previously been identified in two dimensions. In our setting, agents have single-peaked preferences and can manipulate only their response variables. Our main contribution is the discovery of a family of group strategyproof linear regression mechanisms in any number of dimensions, which we call generalized resistant hyperplane mechanisms. The game-theoretic properties of these mechanisms --- and, in fact, their very existence --- are established through a connection to a discrete version of the Ham Sandwich Theorem. 
    more » « less
    Accepted Manuscript Full Text Available