Adam Hesterberg — accepted AI-conference papers

2021

Multidimensional Scaling: Approximation and Complexity

Erik Demaine, Adam Hesterberg, Frederic Koehler, Jayson Lynch, John Urschel

ICML 2021spotlight

Metric Multidimensional scaling (MDS) is a classical method for generating meaningful (non-linear) low-dimensional embeddings of high-dimensional data. MDS has a long history in the statistics, machine learning, and graph drawing communities. In particular, the Kamada-Kawai force-directed graph draw…

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2021

Scalable Equilibrium Computation in Multi-agent Influence Games on Networks

Fotini Christia, Michael Curry, Constantinos Daskalakis, Erik Demaine, John P. Dickerson, MohammadTaghi Hajiaghayi +3

AAAI 2021technical

We provide a polynomial-time, scalable algorithm for equilibrium computation in multi-agent influence games on networks, extending work of Bindel, Kleinberg, and Oren (2015) from the single-agent to the multi-agent setting. In games of influence, agents have limited advertising budget to influence t…

Cited by 3 Source ↗PDF ↗Scholar ↗