A public seminar series from Stitch Fix Algorithms – Algo Hour

Bryan Bischof
- San Francisco, CA

Hello Stitch Fix followers,

Our Algorithms team has a weekly seminar which we call Algo Hour.

We’re excited to tell you about a recent change: we will start allowing people to join in on some of these seminars, and we will also publish them on the web for later viewing.

First up in our new open speaker series is Leland McInnes.

Dr. McInnes is a research mathematician and Data Scientist at the Tutte Institute for Mathematics and Computing in Ottawa, Ontario. He’s well-known for his and collaborators’ creation of the Accelerated HDBSCAN* algorithm, and UMAP – a manifold learning dimension reduction technique.

Nearest Neighbor Descent

Title: Nearest Neighbor Descent(and friends)

Talk Abstract:

Finding nearest neighbors in a dataset is an important fundamental problem for many different machine learning tasks, particularly in unsupervised learning. The problem becomes particularly challenging for higher dimensional data. We’ll look at some of the classical tree based techniques, and look at some of the difficulties that occur. Next we’ll explore Nearest Neighbor Descent and related graph based algorithms, working from very simple intuitions to efficient implementations. The approaches provide a powerful and flexible framework for (approximate) nearest neighbor searching, and, as we will demonstrate, significantly outperform tree based approaches on a number of real world datasets.

Date and Time:

The talk will be Tuesday, May 19th at 2:00PM PST. (9:00 PM UTC)

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