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A Comparison of learning algorithms on the Arcade Learning Environment
Aaron Defazio, Thore Graepel
Reinforcement learning agents have traditionally been evaluated on small toy problems. With advances in computing power and the advent of the Arcade Learning Environment, it is now…
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…
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
Aaron Defazio, Francis Bach, Simon Lacoste-Julien
In this work we introduce a new optimisation method called SAGA in the spirit of SAG, SDCA, MISO and SVRG, a set of recently proposed incremental gradient algorithms with fast line…