Fetching the paper…
Reading the bibliography…
For classification and regression on tabular data, the dominance of gradient-boosted decision trees (GBDTs) has recently been challenged by often much slower deep learning methods with extensive hyperparameter tuning.
Ensemble selection from libraries of models
Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, and Alex Ksikes · 2004
Earlier work this paper cites.
Getting the most out of ensemble selection
Rich Caruana, Art Munson, and Alexandru Niculescu-Mizil · 2006
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
Earlier work this paper cites.
Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
Earlier work this paper cites.
Metalearning: Applications to Data Mining
Pavel Brazdil, Christophe Giraud Carrier, Carlos Soares, and Ricardo Vilalta · 2008
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, and Vincent Dubourg · 2011
Earlier work this paper cites.
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
James Bergstra, Daniel Yamins, and David Cox · 2013
Earlier work this paper cites.
OpenML: Networked science in machine learning
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luis Torgo · 2014
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Learning hyperparameter optimization initializations
Martin Wistuba, Nicolas Schilling, and Lars Schmidt-Thieme · 2015
Earlier work this paper cites.
XGBoost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Earlier work this paper cites.
Entity embeddings of categorical variables
Cheng Guo and Felix Berkhahn · 2016
Earlier work this paper cites.
All you need is a good init
Dmytro Mishkin and Jiri Matas · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger · 2017
Earlier work this paper cites.
LightGBM: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
Earlier work this paper cites.
Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter · 2017
Earlier work this paper cites.
SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
Cyclical learning rates for training neural networks
Leslie N. Smith · 2017
Earlier work this paper cites.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
Earlier work this paper cites.
Ray: A distributed framework for emerging AI applications
Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, and Michael I. Jordan · 2018
Earlier work this paper cites.
CatBoost: Unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2018
Cited alongside, same era.
Meta learning for defaults: Symbolic defaults
Jan N. van Rijn, Florian Pfisterer, Janek Thomas, Andreas Muller, Bernd Bischl, and Joaquin Vanschoren · 2018
Cited alongside, same era.
Joaquin Vanschoren · 2018
Cited alongside, same era.
PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, and Luca Antiga · 2019
Cited alongside, same era.
Tunability: Importance of hyperparameters of machine learning algorithms
Philipp Probst, Anne-Laure Boulesteix, and Bernd Bischl · 2019
Cited alongside, same era.
SMAC3: A versatile Bayesian optimization package for hyperparameter optimization
Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, and Frank Hutter · 2022
Later among the works it cites.
Tabular data: Deep learning is not all you need
Ravid Shwartz-Ziv and Amitai Armon · 2022
Later among the works it cites.
SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C. Bayan Bruss, and Tom Goldstein · 2022
Later among the works it cites.
Trompt: Towards a better deep neural network for tabular data
Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Ting-Wei Chen, and Tien-Hao Chang · 2023
Later among the works it cites.
OpenML-CTR23–A curated tabular regression benchmarking suite
Sebastian Felix Fischer, Matthias Feurer, and Bernd Bischl · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ingo Steinwart · 2019
Cited alongside, same era.
AutoGluon-Tabular: Robust and accurate AutoML for structured data
Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, and Alexander Smola · 2020
Cited alongside, same era.
Autorank: A Python package for automated ranking of classifiers
Steffen Herbold · 2020
Cited alongside, same era.
Fastai: A layered API for deep learning
Jeremy Howard and Sylvain Gugger · 2020
Cited alongside, same era.
TabTransformer: Tabular data modeling using contextual embeddings
Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar Karnin · 2020
Cited alongside, same era.
Mish: A self regularized non-monotonic activation function
Diganta Misra · 2020
Cited alongside, same era.
Hopfield networks is all you need
Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Lukas Gruber, Markus Holzleitner, Thomas Adler, David Kreil, and Michael K. Kopp · 2020
Cited alongside, same era.
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2023
Later among the works it cites.
A framework and benchmark for deep batch active learning for regression
David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart · 2023
Later among the works it cites.
The kernel density integral transformation
Calvin McCarter · 2023
Later among the works it cites.
When do neural nets outperform boosted trees on tabular data?
Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, Vishak Prasad C, Ganesh Ramakrishnan, Micah Goldblum, and Colin White · 2023
Later among the works it cites.
Modern Hopfield networks as memory for iterative learning on tabular data
Bernhard Schäfl, Lukas Gruber, Angela Bitto-Nemling, and Sepp Hochreiter · 2023
Later among the works it cites.
Cross-modal fine-tuning: Align then refine
Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, and Ameet Talwalkar · 2023
Later among the works it cites.
Can a Deep Learning Model be a Sure Bet
Jintai Chen, Jiahuan Yan, Qiyuan Chen, Danny Z. Chen, Jian Wu, and Jimeng Sun · 2024
Closest in time.
AMLB: an AutoML benchmark
Pieter Gijsbers, Marcos LP Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, and Joaquin Vanschoren · 2024
Closest in time.
TabR: Tabular deep learning meets nearest neighbors
Yury Gorishniy, Ivan Rubachev, Nikolay Kartashev, Daniil Shlenskii, Akim Kotelnikov, and Artem Babenko · 2024
Closest in time.
GANDALF: Gated Adaptive Network for Deep Automated Learning of Features
Manu Joseph and Harsh Raj · 2024
Closest in time.
CARTE: pretraining and transfer for tabular learning
Myung Jun Kim, Léo Grinsztajn, and Gaël Varoquaux · 2024
Closest in time.
Towards Quantifying the Effect of Datasets for Benchmarking: A Look at Tabular Machine Learning
Ravin Kohli, Matthias Feurer, Katharina Eggensperger, Bernd Bischl, and Frank Hutter · 2024
Closest in time.
GRANDE: Gradient-based decision tree ensembles for tabular data
Sascha Marton, Stefan Lüdtke, Christian Bartelt, and Heiner Stuckenschmidt · 2024
Closest in time.
TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks
Ivan Rubachev, Nikolay Kartashev, Yury Gorishniy, and Artem Babenko · 2024
Closest in time.
TabRepo: A large scale repository of tabular model evaluations and its AutoML applications
David Salinas and Nick Erickson · 2024
Closest in time.
BiSHop: Bi-directional cellular learning for tabular data with generalized sparse modern Hopfield model
Chenwei Xu, Yu-Chao Huang, Jerry Yao-Chieh Hu, Weijian Li, Ammar Gilani, Hsi-Sheng Goan, and Han Liu · 2024
Closest in time.
A closer look at deep learning on tabular data
Han-Jia Ye, Si-Yang Liu, Hao-Run Cai, Qi-Le Zhou, and De-Chuan Zhan · 2024
Closest in time.