Tamara Broderick
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| title | conference | date |
|---|---|---|
| Nonparametric Bayesian Methods: Models, Algorithms, and Applications I | Foundations of Machine Learning Boot Camp 2017 | 2017-01-23 |
Argument: treat the latent mixture as an infinite parameter so realized cluster count grows with data (roughly log N in the basic Dirichlet case)… | ||
| Nonparametric Bayesian Methods: Models, Algorithms, and Applications IV | Foundations of Machine Learning Boot Camp 2017 | 2017-01-23 |
Explains why node exchangeability (Aldous–Hoover/graphon representation) forces dense or empty growing graphs with edges ~N^2, and proposes edge… | ||
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