Fetching the paper…
Reading the bibliography…
Regression is a powerful tool to accurately predict the outcome metric of a system given a set of parameters, but has traditionally been restricted to methods which are only applicable to a specific task.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E. Terry · 1952
Earlier work this paper cites.
A new family of power transformations to improve normality or symmetry
In-Kwon Yeo and Richard A. Johnson · 2000
Earlier work this paper cites.
Multi-task gaussian process prediction
Edwin V Bonilla, Kian Chai, and Christopher Williams · 2007
Earlier work this paper cites.
Box-cox transformation
Takashi Daimon · 2011
Earlier work this paper cites.
Contextual gaussian process bandit optimization
Andreas Krause and Cheng Ong · 2011
Earlier work this paper cites.
Efficient benchmarking of hyperparameter optimizers via surrogates
Katharina Eggensperger, Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown · 2015
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Earlier work this paper cites.
Google vizier: A service for black-box optimization
Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, and D. Sculley · 2017
Earlier work this paper cites.
Learning memory access patterns
Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, and Parthasarathy Ranganathan · 2018
Earlier work this paper cites.
Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
Taku Kudo and John Richardson · 2018
Earlier work this paper cites.
Tune: A research platform for distributed model selection and training
Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E. Gonzalez, and Ion Stoica · 2018
Earlier work this paper cites.
COCO: The large scale black-box optimization benchmarking (bbob-largescale) test suite
Ouassim Ait ElHara, Konstantinos Varelas, Duc Manh Nguyen, Tea Tusar, Dimo Brockhoff, Nikolaus Hansen, and Anne Auger · 2019
Earlier work this paper cites.
Ithemal: Accurate, portable and fast basic block throughput estimation using deep neural networks
Charith Mendis, Alex Renda, Saman P. Amarasinghe, and Michael Carbin · 2019
Earlier work this paper cites.
Fine-tuning language models from human preferences
Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul F. Christiano, and Geoffrey Irving · 2019
Earlier work this paper cites.
Once-for-all: Train one network and specialize it for efficient deployment
Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han · 2020
Earlier work this paper cites.
Tabtransformer: Tabular data modeling using contextual embeddings
Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar S. Karnin · 2020
Earlier work this paper cites.
Fantastic generalization measures and where to find them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio · 2020
Cited alongside, same era.
Neural architecture performance prediction using graph neural networks
Jovita Lukasik, David Friede, Heiner Stuckenschmidt, and Margret Keuper · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
Cited alongside, same era.
Training independent subnetworks for robust prediction
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, and Dustin Tran · 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.
Competition-level code generation with alphacode
Yujia Li, David H. Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals · 2022
Later among the works it cites.
Introducing chatgpt
OpenAI · 2022
Later among the works it cites.
Large language models encode clinical knowledge
Karan Singhal, Shekoofeh Azizi, Tao Tu, S. Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Kumar Tanwani, Heather Cole-Lewis, Stephen Pfohl, Perry Payne, Martin Seneviratne, Paul Gamble, Chris Kelly, Nathaneal Schärli, Aakanksha Chowdhery, Philip Andrew Mansfield, Blaise Agüera y Arcas, Dale R. Webster, Gregory S. Corrado, Yossi Matias, Katherine Chou, Juraj Gottweis, Nenad Tomasev, Yun Liu, Alvin Rajkomar, Joelle K. Barral, Christopher Semturs, Alan Karthikesalingam, and Vivek Natarajan · 2022
Later among the works it cites.
Open source vizier: Distributed infrastructure and API for reliable and flexible blackbox optimization
Xingyou Song, Sagi Perel, Chansoo Lee, Greg Kochanski, and Daniel Golovin · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A learned performance model for tensor processing units
Samuel J. Kaufman, Phitchaya Mangpo Phothilimthana, Yanqi Zhou, Charith Mendis, Sudip Roy, Amit Sabne, and Mike Burrows · 2021
Cited alongside, same era.
Neural architecture search without training
Joe Mellor, Jack Turner, Amos J. Storkey, and Elliot J. Crowley · 2021
Cited alongside, same era.
Investigating the limitations of the transformers with simple arithmetic tasks
Rodrigo Frassetto Nogueira, Zhiying Jiang, and Jimmy Lin · 2021
Cited alongside, same era.
Few-shot bayesian optimization with deep kernel surrogates
Martin Wistuba and Josif Grabocka · 2021
Cited alongside, same era.
Linear algebra with transformers
François Charton · 2022
Cited alongside, same era.
Towards learning universal hyperparameter optimizers with transformers
Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Richard Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc’Aurelio Ranzato, Sagi Perel, and Nando de Freitas · 2022
Cited alongside, same era.
Deep symbolic regression for recurrent sequences
Stéphane d’Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, and François Charton · 2022
Cited alongside, same era.
Later among the works it cites.
Design-bench: Benchmarks for data-driven offline model-based optimization
Brandon Trabucco, Xinyang Geng, Aviral Kumar, and Sergey Levine · 2022
Later among the works it cites.
Surrogate NAS benchmarks: Going beyond the limited search spaces of tabular NAS benchmarks
Arber Zela, Julien Niklas Siems, Lucas Zimmer, Jovita Lukasik, Margret Keuper, and Frank Hutter · 2022
Later among the works it cites.
Runtime performance prediction for deep learning models with graph neural network
Yanjie Gao, Xianyu Gu, Hongyu Zhang, Haoxiang Lin, and Mao Yang · 2023
Later among the works it cites.
Large language models are zero-shot time series forecasters
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew Gordon Wilson · 2023
Later among the works it cites.
Tabpfn: A transformer that solves small tabular classification problems in a second
Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter · 2023
Later among the works it cites.
Unlocking the transferability of tokens in deep models for tabular data
Qi-Le Zhou, Han-Jia Ye, Le-Ye Wang, and De-Chuan Zhan · 2023
Later among the works it cites.
Transfer learning for bayesian optimization on heterogeneous search spaces
Zhou Fan, Xinran Han, and Zi Wang · 2024
Closest in time.
Gemini: A family of highly capable multimodal models, 2024
Google · 2024
Closest in time.
Automata-based constraints for language model decoding, 2024
Terry Koo, Frederick Liu, and Luheng He · 2024
Closest in time.
The vizier gaussian process bandit algorithm, 2024
Xingyou Song, Qiuyi Zhang, Chansoo Lee, Emily Fertig, Tzu-Kuo Huang, Lior Belenki, Greg Kochanski, Setareh Ariafar, Srinivas Vasudevan, Sagi Perel, and Daniel Golovin · 2024
Closest in time.
Robert Vacareanu, Vlad-Andrei Negru, Vasile Suciu, and Mihai Surdeanu · 2024
Closest in time.