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204da255aea2cd4a75ace6018fad6b4d-Paper.pdf

Neural Information Processing Systems

In this paper, we consider various tree constructions and examine how the choice of parameters affects the generalization error of the resulting random forests as the sample size goes to infinity.





GCOMB: Learning Budget-constrained CombinatorialAlgorithmsoverBillion-sizedGraphs

Neural Information Processing Systems

There has been an increased interest in discovering heuristics for combinatorial problems on graphs through machine learning. While existing techniques have primarily focused onobtaining high-quality solutions, scalability tobillion-sized graphs has not been adequately addressed.