1 citations · 2 across the 2 of their papers we have counts for
2 papers
stat.ML2023★ 1 cited
Machine Learning and the Future of Bayesian Computation
Steven Winter, Trevor Campbell, Lizhen Lin +2
Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they…
stat.ML2016★ 1 cited
Edge-exchangeable graphs and sparsity (NIPS 2016)
Diana Cai, Trevor Campbell, Tamara Broderick
Many popular network models rely on the assumption of (vertex) exchangeability, in which the distribution of the graph is invariant to relabelings of the vertices. However, the Ald…