5 citations · 5 across the 3 of their papers we have counts for
3 papers
stat.ML2017
Ballpark Crowdsourcing: The Wisdom of Rough Group Comparisons
Tom Hope, Dafna Shahaf
Crowdsourcing has become a popular method for collecting labeled training data. However, in many practical scenarios traditional labeling can be difficult for crowdworkers (for exa…
cs.CL2017★ 5 cited
Accelerating Innovation Through Analogy Mining
Tom Hope, Joel Chan, Aniket Kittur +1
The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solut…
stat.ML2016
Ballpark Learning: Estimating Labels from Rough Group Comparisons
Tom Hope, Dafna Shahaf
We are interested in estimating individual labels given only coarse, aggregated signal over the data points. In our setting, we receive sets ("bags") of unlabeled instances with co…