skip to main content

Search for: All records

Creators/Authors contains: "Kulkarni, Adithya"

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. Evans, Robin J. ; Shpitser, Ilya (Ed.)
    Crowdsourcing is an effective and efficient paradigm for obtaining labels for unlabeled corpus employing crowd workers. This work considers the budget allocation problem for a generalized setting on a graph of instances to be labeled where edges encode instance dependencies. Specifically, given a graph and a labeling budget, we propose an optimal policy to allocate the budget among the instances to maximize the overall labeling accuracy. We formulate the problem as a Bayesian Markov Decision Process (MDP), where we define our task as an optimization problem that maximizes the overall label accuracy under budget constraints. Then, we propose a novel stage-wise reward function that considers the effect of worker labels on the whole graph at each timestamp. This reward function is utilized to find an optimal policy for the optimization problem. Theoretically, we show that our proposed policies are consistent when the budget is infinite. We conduct extensive experiments on five real-world graph datasets and demonstrate the effectiveness of the proposed policies to achieve a higher label accuracy under budget constraints. 
    more » « less
    Free, publicly-accessible full text available July 31, 2024
  2. Free, publicly-accessible full text available July 1, 2024
  3. null (Ed.)