https://arxiv.org/abs/2106.11189 close this message Donate to arXiv Please join the Simons Foundation and our generous member organizations in supporting arXiv during our giving campaign September 23-27. 100% of your contribution will fund improvements and new initiatives to benefit arXiv's global scientific community. DONATE [secure site, no need to create account] Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation and member institutions. arXiv.org > cs > arXiv:2106.11189 [ ] Help | Advanced Search [All fields ] Search arXiv Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Machine Learning arXiv:2106.11189 (cs) [Submitted on 21 Jun 2021] Title:Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data Authors:Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka Download PDF Abstract: Tabular datasets are the last "unconquered castle" for deep learning, with traditional ML methods like Gradient-Boosted Decision Trees still performing strongly even against recent specialized neural architectures. In this paper, we hypothesize that the key to boosting the performance of neural networks lies in rethinking the joint and simultaneous application of a large set of modern regularization techniques. As a result, we propose regularizing plain Multilayer Perceptron (MLP) networks by searching for the optimal combination/cocktail of 13 regularization techniques for each dataset using a joint optimization over the decision on which regularizers to apply and their subsidiary hyperparameters. We empirically assess the impact of these regularization cocktails for MLPs on a large-scale empirical study comprising 40 tabular datasets and demonstrate that (i) well-regularized plain MLPs significantly outperform recent state-of-the-art specialized neural network architectures, and (ii) they even outperform strong traditional ML methods, such as XGBoost. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2106.11189 [cs.LG] (or arXiv:2106.11189v1 [cs.LG] for this version) Submission history From: Arlind Kadra [view email] [v1] Mon, 21 Jun 2021 15:27:43 UTC (910 KB) Full-text links: Download: * PDF * Other formats (license) Current browse context: cs.LG < prev | next > new | recent | 2106 Change to browse by: cs References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export bibtex citation Loading... Bibtex formatted citation x [loading... ] Data provided by: Bookmark BibSonomy logo Mendeley logo Reddit logo ScienceWISE logo (*) Bibliographic Tools Bibliographic and Citation Tools [ ] Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) [ ] Litmaps Toggle Litmaps (What is Litmaps?) ( ) Code & Data Code and Data Associated with this Article [ ] arXiv Links to Code Toggle arXiv Links to Code & Data (What is Links to Code & Data?) ( ) Related Papers Recommenders and Search Tools [ ] Connected Papers Toggle Connected Papers (What is Connected Papers?) [ ] Core recommender toggle CORE Recommender (What is CORE?) ( ) About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs and how to get involved. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) * About * Help * Click here to contact arXiv Contact * Click here to subscribe Subscribe * Copyright * Privacy Policy * Web Accessibility Assistance * arXiv Operational Status Get status notifications via email or slack