paper-conference

Exclusion Zones of Instant Runoff Voting

Recent research on instant runoff voting (IRV) shows that it exhibits a striking combinatorial property in one-dimensional preference …

Kiran Tomlinson, Johan Ugander, Jon Kleinberg

Lost in Transmission: When and Why LLMs Fail to Reason Globally

Despite their many successes, transformer-based large language models (LLMs) continue to struggle with tasks that require complex …

Tobias Schnabel, Kiran Tomlinson, Adith Swaminathan, Jennifer Neville

When the Universe is Too Big: Bounding Consideration Probabilities for Plackett-Luce Rankings

The widely used Plackett-Luce ranking model assumes that individuals rank items by making repeated choices from a universe of items. …

Ben Aoki-Sherwood, Catherine Bregou, David Liben-Nowell, Kiran Tomlinson, Thomas Zeng

Replicating Electoral Success

A core tension in the study of plurality elections is the clash between the classic Hotelling-Downs model, which predicts that two …

Kiran Tomlinson, Tanvi Namjoshi, Johan Ugander, Jon Kleinberg

The Moderating Effect of Instant Runoff Voting

We prove that instant runoff voting favors moderate candidates in a way that plurality does not in a 1-dimensional preference model.

Kiran Tomlinson, Johan Ugander, Jon Kleinberg

Workplace Recommendation with Temporal Network Objectives

Workplace communication software such as Microsoft Teams, Slack, and Google Workspace have become integral to workplace collaboration, …

Kiran Tomlinson, Jennifer Neville, Longqi Yang, Mengting Wan, Cao Lu

Ballot Length in Instant Runoff Voting

We theoretically and empirically investigate the impact of ballot length in instant runoff voting elections.

Kiran Tomlinson, Johan Ugander, Jon Kleinberg

Learning Interpretable Feature Context Effects in Discrete Choice

We introduce a broad class of context effects in discrete choice and show that these effects are common in choice data, including choices coming from social networks.

Kiran Tomlinson, Austin R. Benson

Choice Set Confounding in Discrete Choice

We show how confounding can mislead choice models and adapt several causal inference methods to train unbiased models from observational data.

Kiran Tomlinson, Johan Ugander, Austin R. Benson

Choice Set Confounding in Discrete Choice

Choice Set Optimization Under Discrete Choice Models of Group Decisions

We study the problem of finding the optimal set of alternatives to introduce to a group to minimize their disagreement and other related problems.

Kiran Tomlinson, Austin R. Benson