9 citations · 19 across the 8 of their papers we have counts for
8 papers
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…
Rank-heterogeneous Preference Models for School Choice
Amel Awadelkarim, Arjun Seshadri, Itai Ashlagi +2
School choice mechanism designers use discrete choice models to understand and predict families' preferences. The most widely-used choice model, the multinomial logit (MNL), is lin…
HandsOff: Labeled Dataset Generation With No Additional Human Annotations
Austin Xu, Mariya I. Vasileva, Achal Dave +1
Recent work leverages the expressive power of generative adversarial networks (GANs) to generate labeled synthetic datasets. These dataset generation methods often require new anno…
RecXplainer: Amortized Attribute-based Personalized Explanations for Recommender Systems
Sahil Verma, Chirag Shah, John P. Dickerson +3
Recommender systems influence many of our interactions in the digital world -- impacting how we shop for clothes, sorting what we see when browsing YouTube or TikTok, and determini…
Contrastive Learning for Interactive Recommendation in Fashion
Karin Sevegnani, Arjun Seshadri, Tian Wang +4
Recommender systems and search are both indispensable in facilitating personalization and ease of browsing in online fashion platforms. However, the two tools often operate indepen…
Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice
Arjun Seshadri, Johan Ugander
The Multinomial Logit (MNL) model and the axiom it satisfies, the Independence of Irrelevant Alternatives (IIA), are together the most widely used tools of discrete choice. The MNL…
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…