36 citations · 70 across the 7 of their papers we have counts for
7 papers
Learning Rich Rankings
Arjun Seshadri, Stephen Ragain, Johan Ugander
Although the foundations of ranking are well established, the ranking literature has primarily been focused on simple, unimodal models, e.g. the Mallows and Plackett-Luce models, t…
MiCRO: Multi-interest Candidate Retrieval Online
Frank Portman, Stephen Ragain, Ahmed El-Kishky
Providing personalized recommendations in an environment where items exhibit ephemerality and temporal relevancy (e.g. in social media) presents a few unique challenges: (1) induct…
Birdwatch: Crowd Wisdom and Bridging Algorithms can Inform Understanding and Reduce the Spread of Misinformation
Stefan Wojcik, Sophie Hilgard, Nick Judd +5
We present an approach for selecting objectively informative and subjectively helpful annotations to social media posts. We draw on data from on an online environment where contrib…
Choosing to Rank
Stephen Ragain, Johan Ugander
Ranking data arises in a wide variety of application areas but remains difficult to model, learn from, and predict. Datasets often exhibit multimodality, intransitivity, or incompl…
Improving pairwise comparison models using Empirical Bayes shrinkage
Stephen Ragain, Alexander Peysakhovich, Johan Ugander
Comparison data arises in many important contexts, e.g. shopping, web clicks, or sports competitions. Typically we are given a dataset of comparisons and wish to train a model to m…
Pairwise Choice Markov Chains
Stephen Ragain, Johan Ugander
As datasets capturing human choices grow in richness and scale -- particularly in online domains -- there is an increasing need for choice models that escape traditional choice-the…
A Leapfrog Strategy for Pursuit-Evasion in a Polygonal Environment
Brendan Ames, Andrew Beveridge, Rosalie Carlson +4
We study pursuit-evasion in a polygonal environment with polygonal obstacles. In this turn based game, an evader is chased by pursuers . The players ha…