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2002.11887
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Using a thousand optimization tasks to learn hyperparameter search strategies
27 February 2020
Luke Metz
Niru Maheswaranathan
Ruoxi Sun
C. Freeman
Ben Poole
Jascha Narain Sohl-Dickstein
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Papers citing
"Using a thousand optimization tasks to learn hyperparameter search strategies"
37 / 37 papers shown
Cost-Sensitive Freeze-thaw Bayesian Optimization for Efficient Hyperparameter Tuning
Dong Bok Lee
Aoxuan Silvia Zhang
Byungjoo Kim
Junhyeon Park
Steven Adriaensen
J. H. Lee
Sung Ju Hwang
Hae Beom Lee
188
2
0
24 Oct 2025
Tune My Adam, Please!
Theodoros Athanasiadis
Steven Adriaensen
Samuel G. Müller
Frank Hutter
163
1
0
27 Aug 2025
LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously Thought
Cheng Yan
Felix Mohr
Tom Viering
375
1
0
21 May 2025
One Search Fits All: Pareto-Optimal Eco-Friendly Model Selection
Filippo Betello
Antonio Purificato
Vittoria Vineis
Gabriele Tolomei
Fabrizio Silvestri
501
1
0
02 May 2025
Training neural networks faster with minimal tuning using pre-computed lists of hyperparameters for NAdamW
Sourabh Medapati
Priya Kasimbeg
Shankar Krishnan
Naman Agarwal
George E. Dahl
260
0
0
06 Mar 2025
Celo: Training Versatile Learned Optimizers on a Compute Diet
A. Moudgil
Boris Knyazev
Guillaume Lajoie
Eugene Belilovsky
1.1K
0
0
22 Jan 2025
Narrowing the Focus: Learned Optimizers for Pretrained Models
Gus Kristiansen
Mark Sandler
A. Zhmoginov
Nolan Miller
Anirudh Goyal
Jihwan Lee
Max Vladymyrov
356
2
0
17 Aug 2024
Implicit Neural Image Field for Biological Microscopy Image Compression
Gaole Dai
Cheng-Ching Tseng
Qingpo Wuwu
Rongyu Zhang
Shaokang Wang
...
Yu Zhou
A. A. Tuz
Matthias Gunzer
Jianxu Chen
Shanghang Zhang
298
1
0
29 May 2024
Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation
Dong Bok Lee
Aoxuan Silvia Zhang
Byung-Hoon Kim
Junhyeon Park
Juho Lee
Sung Ju Hwang
Haebeom Lee
446
4
0
28 May 2024
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
Herilalaina Rakotoarison
Steven Adriaensen
Neeratyoy Mallik
Samir Garibov
Eddie Bergman
Frank Hutter
AI4CE
446
28
0
25 Apr 2024
Should I try multiple optimizers when fine-tuning pre-trained Transformers for NLP tasks? Should I tune their hyperparameters?
Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2024
Nefeli Gkouti
Prodromos Malakasiotis
Stavros Toumpis
Ion Androutsopoulos
279
6
0
10 Feb 2024
NeuroEvoBench: Benchmarking Evolutionary Optimizers for Deep Learning Applications
Neural Information Processing Systems (NeurIPS), 2023
Robert Tjarko Lange
Yujin Tang
Yingtao Tian
ELM
322
4
0
04 Nov 2023
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks
Neural Information Processing Systems (NeurIPS), 2023
Steven Adriaensen
Herilalaina Rakotoarison
Samuel G. Müller
Katharina Eggensperger
BDL
293
46
0
31 Oct 2023
Deep Pipeline Embeddings for AutoML
Knowledge Discovery and Data Mining (KDD), 2023
Sebastian Pineda Arango
Josif Grabocka
391
4
0
23 May 2023
Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single
International Conference on Machine Learning (ICML), 2023
Paul Vicol
Zico Kolter
Kevin Swersky
234
8
0
21 Apr 2023
Judging Adam: Studying the Performance of Optimization Methods on ML4SE Tasks
D. Pasechnyuk
Anton Prazdnichnykh
Mikhail Evtikhiev
T. Bryksin
309
1
0
06 Mar 2023
Denoising Diffusion Samplers
International Conference on Learning Representations (ICLR), 2023
Francisco Vargas
Will Grathwohl
Arnaud Doucet
DiffM
427
133
0
27 Feb 2023
Learning to Generalize Provably in Learning to Optimize
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Junjie Yang
Tianlong Chen
Mingkang Zhu
Fengxiang He
Dacheng Tao
Yitao Liang
Zinan Lin
269
11
0
22 Feb 2023
Learning to Optimize for Reinforcement Learning
Qingfeng Lan
Rupam Mahmood
Shuicheng Yan
Zhongwen Xu
OffRL
503
10
0
03 Feb 2023
Scaling Laws for Hyperparameter Optimization
Neural Information Processing Systems (NeurIPS), 2023
Arlind Kadra
Maciej Janowski
Martin Wistuba
Josif Grabocka
544
22
0
01 Feb 2023
General-Purpose In-Context Learning by Meta-Learning Transformers
Louis Kirsch
James Harrison
Jascha Narain Sohl-Dickstein
Luke Metz
540
108
0
08 Dec 2022
VeLO: Training Versatile Learned Optimizers by Scaling Up
Luke Metz
James Harrison
C. Freeman
Amil Merchant
Lucas Beyer
...
Naman Agrawal
Ben Poole
Igor Mordatch
Adam Roberts
Jascha Narain Sohl-Dickstein
361
78
0
17 Nov 2022
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases
Neural Information Processing Systems (NeurIPS), 2022
James Harrison
Luke Metz
Jascha Narain Sohl-Dickstein
262
33
0
22 Sep 2022
Practical tradeoffs between memory, compute, and performance in learned optimizers
Luke Metz
C. Freeman
James Harrison
Niru Maheswaranathan
Jascha Narain Sohl-Dickstein
516
39
0
22 Mar 2022
Supervising the Multi-Fidelity Race of Hyperparameter Configurations
Neural Information Processing Systems (NeurIPS), 2022
Martin Wistuba
Arlind Kadra
Josif Grabocka
328
21
0
20 Feb 2022
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies
International Conference on Machine Learning (ICML), 2021
Paul Vicol
Luke Metz
Jascha Narain Sohl-Dickstein
345
77
0
27 Dec 2021
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
Katharina Eggensperger
Philip Muller
Neeratyoy Mallik
Matthias Feurer
René Sass
Aaron Klein
Noor H. Awad
Marius Lindauer
Katharina Eggensperger
487
130
0
14 Sep 2021
Meta-Learning for Symbolic Hyperparameter Defaults
Pieter Gijsbers
Florian Pfisterer
Jan N. van Rijn
J. Herbinger
Joaquin Vanschoren
271
11
0
10 Jun 2021
Meta Learning Black-Box Population-Based Optimizers
H. Gomes
B. Léger
Christian Gagné
361
14
0
05 Mar 2021
Parallel Training of Deep Networks with Local Updates
Michael Laskin
Luke Metz
Seth Nabarrao
Mark Saroufim
Badreddine Noune
Carlo Luschi
Jascha Narain Sohl-Dickstein
Pieter Abbeel
FedML
354
31
0
07 Dec 2020
Hyperparameter Transfer Across Developer Adjustments
Daniel Stoll
Jörg Franke
Diane Wagner
Simon Selg
Katharina Eggensperger
255
14
0
25 Oct 2020
How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers
Yuanhao Xiong
Xuanqing Liu
Li-Cheng Lan
Yang You
Si Si
Cho-Jui Hsieh
OOD
267
2
0
19 Oct 2020
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves
Luke Metz
Niru Maheswaranathan
C. Freeman
Ben Poole
Jascha Narain Sohl-Dickstein
421
70
0
23 Sep 2020
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
Journal of machine learning research (JMLR), 2020
Matthias Feurer
Katharina Eggensperger
Stefan Falkner
Marius Lindauer
Katharina Eggensperger
589
369
0
08 Jul 2020
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers
Robin M. Schmidt
Frank Schneider
Philipp Hennig
ODL
912
195
0
03 Jul 2020
Adaptive Gradient Methods Converge Faster with Over-Parameterization (but you should do a line-search)
Sharan Vaswani
I. Laradji
Frederik Kunstner
S. Meng
Mark Schmidt
Damien Scieur
370
30
0
11 Jun 2020
Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach
Ryo Karakida
S. Akaho
S. Amari
FedML
682
169
0
04 Jun 2018
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