John Urschel

2 accepted 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…

Cited by 17 Source ↗PDF ↗Scholar ↗

2017

Learning Determinantal Point Processes with Moments and Cycles

John Urschel, Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet

ICML 2017poster

Determinantal Point Processes (DPPs) are a family of probabilistic models that have a repulsive behavior, and lend themselves naturally to many tasks in machine learning where returning a diverse set of objects is important. While there are fast algorithms for sampling, marginalization and condition…

Cited by 33 Source ↗PDF ↗Scholar ↗