Marc E. Pfetsch, Sebastian Pokutta
Published 2020-02-11Version 1
Recently non-convex optimization approaches for solving machine learning problems have gained significant attention. In this paper we explore non-convex boosting in classification by means of integer programming and demonstrate real-world practicability of the approach while circumventing shortcomings of convex boosting approaches. We report results that are comparable to or better than the current state-of-the-art.
Accelerated Nonnegative Tensor Completion via Integer Programming
Reinforcement Learning for Integer Programming: Learning to Cut
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming
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