607 citations · 625 across the 4 of their papers we have counts for
5 papers
Regression-based proximal causal inference for right-censored time-to-event data
Kendrick Li, George C. Linderman, Xu Shi +1
Unmeasured confounding is one of the major concerns in causal inference from observational data. Proximal causal inference (PCI) is an emerging methodological framework to detect a…
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations
Dmitry Kobak, George Linderman, Stefan Steinerberger +2
T-distributed stochastic neighbour embedding (t-SNE) is a widely used data visualisation technique. It differs from its predecessor SNE by the low-dimensional similarity kernel: th…
Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding
George C. Linderman, Manas Rachh, Jeremy G. Hoskins +2
t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for dimensionality reduction and visualization that has become widely popular in recent years. Efficient impleme…
Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science
George C. Linderman, Gal Mishne, Yuval Kluger +1
If we pick random points uniformly in and connect each point to its nearest neighbors, then it is well known that there exists a giant connected component with hi…
Clustering with t-SNE, provably
George C. Linderman, Stefan Steinerberger
t-distributed Stochastic Neighborhood Embedding (t-SNE), a clustering and visualization method proposed by van der Maaten & Hinton in 2008, has rapidly become a standard tool in a…