144 citations · 324 across the 35 of their papers we have counts for
4 papers · 1 filter
Projecting Markov Random Field Parameters for Fast Mixing
Xianghang Liu, Justin Domke
Markov chain Monte Carlo (MCMC) algorithms are simple and extremely powerful techniques to sample from almost arbitrary distributions. The flaw in practice is that it can take a la…
Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems
Aaron J. Defazio, Tibério S. Caetano, Justin Domke
Recent advances in optimization theory have shown that smooth strongly convex finite sums can be minimized faster than by treating them as a black box "batch" problem. In this work…
Structured Learning via Logistic Regression
Justin Domke
A successful approach to structured learning is to write the learning objective as a joint function of linear parameters and inference messages, and iterate between updates to each…
Projecting Ising Model Parameters for Fast Mixing
Justin Domke, Xianghang Liu
Inference in general Ising models is difficult, due to high treewidth making tree-based algorithms intractable. Moreover, when interactions are strong, Gibbs sampling may take expo…