NIPS Proceedingsβ

Bo Waggoner

8 Papers

  • An Embedding Framework for Consistent Polyhedral Surrogates (2019)
  • Equal Opportunity in Online Classification with Partial Feedback (2019)
  • Toward a Characterization of Loss Functions for Distribution Learning (2019)
  • A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem (2018)
  • Bounded-Loss Private Prediction Markets (2018)
  • Local Differential Privacy for Evolving Data (2018)
  • Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM (2017)
  • A Market Framework for Eliciting Private Data (2015)