3 papers
stat.ML2026
On the continuum limit of t-SNE for data visualization
Jeff Calder, Zhonggan Huang, Ryan Murray +1
This work is concerned with the continuum limit of a graph-based data visualization technique called the t-Distributed Stochastic Neighbor Embedding (t-SNE), which is widely used f…
math.ST2024
Large data limits and scaling laws for tSNE
Ryan Murray, Adam Pickarski
This work considers large-data asymptotics for t-distributed stochastic neighbor embedding (tSNE), a widely-used non-linear dimension reduction algorithm. We identify an appropriat…
stat.ML2024
On Probabilistic Embeddings in Optimal Dimension Reduction
Ryan Murray, Adam Pickarski
Dimension reduction algorithms are a crucial part of many data science pipelines, including data exploration, feature creation and selection, and denoising. Despite their wide util…