9 citations · 19 across the 8 of their papers we have counts for
3 papers · 1 filter
Bypassing Skip-Gram Negative Sampling: Dimension Regularization as a More Efficient Alternative for Graph Embeddings
David Liu, Arjun Seshadri, Tina Eliassi-Rad +1
A wide range of graph embedding objectives decompose into two components: one that enforces similarity, attracting the embeddings of nodes that are perceived as similar, and anothe…
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…
Discovering Context Effects from Raw Choice Data
Arjun Seshadri, Alexander Peysakhovich, Johan Ugander
Many applications in preference learning assume that decisions come from the maximization of a stable utility function. Yet a large experimental literature shows that individual ch…