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Yes, that's the one: https://arxiv.org/pdf/1906.06852 https://arxiv.org/pdf/1906.06852
by abhgh 2y ago
Yes, that's the one: https://arxiv.org/pdf/1906.06852 https://arxiv.org/pdf/1906.06852
- aspenmayer 2y agoI copied the DOI for convenience but they’re the same paper. I have no formal math background really so I can’t speak to your methods but I appreciate that you have shared your work freely. Did you have any issues defending your thesis due to the issues you described above related to publishing? Noticed a typo in your abstract: “Maybe” should be “may be” in sentence below (italics): > We show that this technique addresses the above challenges: (a) it arrests the reduction in accuracy that comes from shrinking a model (in some cases we observe ~ 100% improvement over baselines), and also, (b) that this maybe applied with no change across model families with different notions of size; results are shown for Decision Trees, Linear Probability models and Gradient Boosted Models.
- abhgh 2y agoYes, it did come up during my defense, but it was deemed not to be a concern since I had one prior paper [1] (the original one in this thread of work, the paper I linked above was an improvement over it), and my advisor (co-author on both papers) vouched for the quality of the work. Thank you for pointing out the typo - will fix it! [1] https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2020.00003/pdf https://www.frontiersin.org/journals/artificial-intelligence...