Fetching the paper…
Reading the bibliography…
As the field of automated machine learning (AutoML) advances, it becomes increasingly important to incorporate domain knowledge into these systems.
The autofeat python library for automatic feature engineering and selection
Franziska Horn, Robert Pack, and Michael Rieger · 1901
Earlier work this paper cites.
Openml-python: an extensible python api for openml
Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Mueller, Joaquin Vanschoren, and Frank Hutter · 1911
Earlier work this paper cites.
Principal component analysis
Svante Wold, Kim Esbensen, and Paul Geladi · 1987
Earlier work this paper cites.
Random forests
Leo Breiman · 2001
Earlier work this paper cites.
Data Structures for Statistical Computing in Python
Wes McKinney · 2010
Earlier work this paper cites.
Openml: Networked science in machine learning
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luis Torgo · 2013
Earlier work this paper cites.
Openml: networked science in machine learning
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luís Torgo · 2014
Earlier work this paper cites.
Deep feature synthesis: Towards automating data science endeavors
James Max Kanter and Kalyan Veeramachaneni · 2015
Earlier work this paper cites.
Cognito: Automated feature engineering for supervised learning
Udayan Khurana, Deepak Turaga, Horst Samulowitz, and Srinivasan Parthasrathy · 2016
Earlier work this paper cites.
Learning feature engineering for classification
Fatemeh Nargesian, Horst Samulowitz, Udayan Khurana, Elias B Khalil, and Deepak S Turaga · 2017
Earlier work this paper cites.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Feature engineering for predictive modeling using reinforcement learning
Udayan Khurana, Horst Samulowitz, and Deepak Turaga · 2018
Earlier work this paper cites.
Automated Machine Learning: Methods, Systems, Challenges
F. Hutter, L. Kotthoff, and J. Vanschoren (eds.) · 2019
Cited alongside, same era.
Automatic feature engineering by deep reinforcement learning
Jianyu Zhang, Jianye Hao, Françoise Fogelman-Soulié, and Zan Wang · 2019
Cited alongside, same era.
Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
Cited alongside, same era.
The state of data science 2020
Anaconda · 2020
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
A human-in-the-loop perspective on automl: Milestones and the road ahead
Tabpfn: A transformer that solves small tabular classification problems in a second
Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter · 2022
Later among the works it cites.
Can foundation models wrangle your data?, 2022
Avanika Narayan, Ines Chami, Laurel Orr, Simran Arora, and Christopher Ré · 2022
Later among the works it cites.
Towards parameter-efficient automation of data wrangling tasks with prefix-tuning
David Vos, Till Döhmen, and Sebastian Schelter · 2022
Later among the works it cites.
Ai generated code creates a new security attack vector, April 2023
Adam Crockett · 2023
Closest in time.
Tabllm: Few-shot classification of tabular data with large language models, 2023
Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, and David Sontag · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Doris Jung-Lin Lee and Stephen Macke · 2020
Cited alongside, same era.
Large image datasets: A pyrrhic win for computer vision?, 2020
Vinay Uday Prabhu and Abeba Birhane · 2020
Cited alongside, same era.
Accounting for variance in machine learning benchmarks
Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Nazanin Mohammadi Sepahvand, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Tal Arbel, Chris Pal, Gael Varoquaux, and Pascal Vincent · 2021
Cited alongside, same era.
Measuring mathematical problem solving with the MATH dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
Cited alongside, same era.
Ai and the everything in the whole wide world benchmark, 2021
Inioluwa Deborah Raji, Emily M. Bender, Amandalynne Paullada, Emily Denton, and Alex Hanna · 2021
Cited alongside, same era.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
Cited alongside, same era.
Automating data science
Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H Hoos, Padhraic Smyth, and Christopher KI Williams · 2022
Cited alongside, same era.
OpenAI · 2023
Closest in time.
GPT-3 can’t count syllables - or doesn’t “get” haiku
OpenAI Community · 2023
Closest in time.
Ai code generation and cybersecurity, April 2023
Chris Rohlf · 2023
Closest in time.
Challenging the appearance of machine intelligence: Cognitive bias in llms and best practices for adoption, 2023
Alaina N. Talboy and Elizabeth Fuller · 2023
Closest in time.
Can fairness be automated? guidelines and opportunities for fairness-aware automl, 2023
Hilde Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, and Frank Hutter · 2023
Closest in time.
Chain-of-thought prompting elicits reasoning in large language models, 2023
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou · 2023
Closest in time.