VLDB 2026 Research / reviewers in the wild / expert
Sam Patterson
dblp:28/11468
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2013
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Probabilistic and Bayesian machine learning · 41% Information extraction and text analysis · 41% Kernel, tree and ensemble methods · 18% | |
| Theoretical computer science
1 paper |
Mathematical optimization · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning › monte carlo methods › markov chain monte carlo
langevin dynamics |
0.2 | 1 | 2013 | Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex · NIPS 2013 |
Natural language and speech › Information extraction and text analysis › topic model
latent dirichlet allocation |
0.2 | 1 | 2013 | Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex · NIPS 2013 |
Machine learning › Probabilistic and Bayesian machine learning › monte carlo methods
markov chain monte carlo |
0.2 | 1 | 2013 | Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex · NIPS 2013 |
Natural language and speech › Information extraction and text analysis
topic model |
0.2 | 1 | 2013 | Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex · NIPS 2013 |
Machine learning › Kernel, tree and ensemble methods › kernel embedding
conditional mean embedding |
0.1 | 1 | 2012 | Conditional mean embeddings as regressors · ICML 2012 |
Mathematical optimization
riemannian optimization |
0.0 | 1 | 2013 | Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex · NIPS 2013 |
Methods — techniques the papers use, named apart from their topics
stochastic gradient riemannian langevin dynamics · 0.3online variational bayes · 0.3regression · 0.1kernel methods · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Stochastic Gradient Riemannian Langevin Dynamics on the Probability SimplexabstractIn this paper we investigate the use of Langevin Monte Carlo methods on the probability simplex and propose a new method, Stochastic gradient Riemannian Langevin dynamics, which is simple to implement and can be applied online. We apply this method to latent Dirichlet allocation in an online setting, and demonstrate that it achieves substantial performance improvements to the state of the art online variational Bayesian methods. Sam Patterson, Yee Whye Teh |
NIPS | 1 |
| 2012 | Conditional mean embeddings as regressors
Steffen Grünewälder, Guy Lever, Arthur Gretton, Luca Baldassarre, Sam Patterson, Massimiliano Pontil |
ICML | 5 |