173 citations · 305 across the 14 of their papers we have counts for
14 papers
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…
A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation
Aaron J. Defazio, Tiberio S. Caetano
A key problem in statistics and machine learning is the determination of network structure from data. We consider the case where the structure of the graph to be reconstructed is k…
A Hybrid Loss for Multiclass and Structured Prediction
Qinfeng Shi, Mark Reid, Tiberio Caetano +2
We propose a novel hybrid loss for multiclass and structured prediction problems that is a convex combination of a log loss for Conditional Random Fields (CRFs) and a multiclass hi…
A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training
Aaron Defazio, Tiberio Caetano
Item neighbourhood methods for collaborative filtering learn a weighted graph over the set of items, where each item is connected to those it is most similar to. The prediction of…
Conditional Random Fields and Support Vector Machines: A Hybrid Approach
Qinfeng Shi, Mark D. Reid, Tiberio Caetano
We propose a novel hybrid loss for multiclass and structured prediction problems that is a convex combination of log loss for Conditional Random Fields (CRFs) and a multiclass hing…
An expected-case sub-cubic solution to the all-pairs shortest path problem in R
Julian J. McAuley, Tibério S. Caetano
It has been shown by Alon et al. that the so-called 'all-pairs shortest-path' problem can be solved in O((MV)^2.688 * log^3(V)) for graphs with V vertices, with integer distances b…