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1406.3896
Cited By
Freeze-Thaw Bayesian Optimization
16 June 2014
Kevin Swersky
Jasper Snoek
Ryan P. Adams
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Papers citing
"Freeze-Thaw Bayesian Optimization"
50 / 131 papers shown
Cost-Sensitive Freeze-thaw Bayesian Optimization for Efficient Hyperparameter Tuning
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Junhyeon Park
Steven Adriaensen
J. H. Lee
Sung Ju Hwang
Hae Beom Lee
190
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Zero-Shot Performance Prediction for Probabilistic Scaling Laws
Viktoria Schram
Markus Hiller
Daniel Beck
Trevor Cohn
176
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0
19 Oct 2025
Scaling with Collapse: Efficient and Predictable Training of LLM Families
Shane Bergsma
Bin Claire Zhang
Nolan Dey
Shaheer Muhammad
Gurpreet Gosal
Joel Hestness
197
4
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29 Sep 2025
Tune My Adam, Please!
Theodoros Athanasiadis
Steven Adriaensen
Samuel G. Müller
Frank Hutter
167
1
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27 Aug 2025
You Only Train Once
Christos Sakaridis
224
0
0
04 Jun 2025
Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
Dongwoo Lee
Dong Bok Lee
Steven Adriaensen
Juho Lee
Sung Ju Hwang
Frank Hutter
Seon Joo Kim
Hae Beom Lee
BDL
449
0
0
29 May 2025
LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously Thought
Cheng Yan
Felix Mohr
Tom Viering
376
1
0
21 May 2025
When to retrain a machine learning model
Regol Florence
Schwinn Leo
Sprague Kyle
Coates Mark
Markovich Thomas
OffRL
321
7
0
20 May 2025
Data Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework
Thomson Yen
Andrew Siah
Haozhe Chen
Tianyi Peng
Daniel Guetta
Hongseok Namkoong
462
4
0
26 Mar 2025
Information-theoretic Bayesian Optimization: Survey and Tutorial
Eduardo C. Garrido-Merchán
482
3
0
22 Jan 2025
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure
Jihao Andreas Lin
Sebastian Ament
Maximilian Balandat
E. Bakshy
BDL
208
8
0
11 Oct 2024
Meta-Learning from Learning Curves for Budget-Limited Algorithm Selection
Pattern Recognition Letters (PR), 2024
Manh Hung Nguyen
Lisheng Sun-Hosoya
Isabelle M Guyon
269
1
0
10 Oct 2024
Adaptive Learn-then-Test: Statistically Valid and Efficient Hyperparameter Selection
Matteo Zecchin
Sangwoo Park
Osvaldo Simeone
LM&MA
733
10
0
24 Sep 2024
FastBO: Fast HPO and NAS with Adaptive Fidelity Identification
Jiantong Jiang
Lin Wang
466
1
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01 Sep 2024
Beyond Trend Following: Deep Learning for Market Trend Prediction
Fernando Berzal
Alberto Garcia
244
0
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10 Jun 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
450
4
0
28 May 2024
Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization
Thomas Nagler
Lennart Schneider
J. Herbinger
Matthias Feurer
238
4
0
24 May 2024
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
Herilalaina Rakotoarison
Steven Adriaensen
Neeratyoy Mallik
Samir Garibov
Eddie Bergman
Frank Hutter
AI4CE
459
28
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25 Apr 2024
NeuroLGP-SM: Scalable Surrogate-Assisted Neuroevolution for Deep Neural Networks
Fergal Stapleton
Edgar Galván López
333
2
0
12 Apr 2024
NeuroLGP-SM: A Surrogate-assisted Neuroevolution Approach using Linear Genetic Programming
Fergal Stapleton
Brendan Cody-Kenny
Edgar Galván López
278
3
0
28 Mar 2024
Strong convexity-guided hyper-parameter optimization for flatter losses
Rahul Yedida
Snehanshu Saha
428
0
0
07 Feb 2024
Can we infer the presence of Differential Privacy in Deep Learning models' weights? Towards more secure Deep Learning
Daniel Jiménez-López
Daniel
Nuria Rodríguez Barroso
Nuria
M. V. Luzón
M. Victoria
Francisco Herrera
Francisco
AAML
217
0
0
20 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
298
47
0
31 Oct 2023
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning
Neural Information Processing Systems (NeurIPS), 2023
Neeratyoy Mallik
Eddie Bergman
Carl Hvarfner
Daniel Stoll
Maciej Janowski
Marius Lindauer
Luigi Nardi
Katharina Eggensperger
357
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21 Jun 2023
Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization
Yin-Ting Liao
Hengrui Luo
A. Ma
294
3
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01 Jun 2023
Optimizing Hyperparameters with Conformal Quantile Regression
International Conference on Machine Learning (ICML), 2023
David Salinas
Jacek Golebiowski
Aaron Klein
Matthias Seeger
Cédric Archambeau
296
13
0
05 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
237
8
0
21 Apr 2023
Hyper-parameter Tuning for Adversarially Robust Models
Pedro Mendes
Paolo Romano
David Garlan
AAML
278
3
0
05 Apr 2023
Learning to Rank Normalized Entropy Curves with Differentiable Window Transformation
Hanyang Liu
Shuai Yang
Feng Qi
Shuaiwen Wang
353
0
0
25 Jan 2023
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round
Manh Hung Nguyen
Lisheng Sun
Nathan Grinsztajn
Isabelle M Guyon
269
1
0
04 Aug 2022
Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems
IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2022
Songjie Yang
Baojuan Liu
Zhiqin Hong
Zhong-pei Zhang
310
13
0
28 Jul 2022
Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Syrine Belakaria
J. Doppa
Nicolò Fusi
Rishit Sheth
355
10
0
25 Jun 2022
A Probabilistic Machine Learning Approach to Scheduling Parallel Loops with Bayesian Optimization
IEEE Transactions on Parallel and Distributed Systems (TPDS), 2021
Kyurae Kim
Youngjae Kim
Sungyong Park
319
14
0
12 Jun 2022
FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization
Zhen Wang
Weirui Kuang
Ce Zhang
Bolin Ding
Yaliang Li
FedML
564
17
0
08 Jun 2022
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning
Knowledge Discovery and Data Mining (KDD), 2022
Yan Zhao
Yu Shen
Huaijun Jiang
Wentao Zhang
Zhi-Xin Yang
Ce Zhang
Tengjiao Wang
224
19
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06 Jun 2022
Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression
Yi Ding
Alex Renda
Ahsan Pervaiz
Michael Carbin
Henry Hoffmann
170
4
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11 Apr 2022
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Neural Information Processing Systems (NeurIPS), 2022
Krishnateja Killamsetty
Guttu Sai Abhishek
Aakriti
A. Evfimievski
Yatin Nandwani
Ganesh Ramakrishnan
Rishabh K. Iyer
166
32
0
15 Mar 2022
Practitioner Motives to Use Different Hyperparameter Optimization Methods
ACM Transactions on Computer-Human Interaction (TOCHI), 2022
Niclas Kannengießer
Niklas Hasebrook
Felix Morsbach
Marc-André Zöller
Jörg Franke
Marius Lindauer
Katharina Eggensperger
Ali Sunyaev
418
4
0
03 Mar 2022
Amortized Proximal Optimization
Neural Information Processing Systems (NeurIPS), 2022
Juhan Bae
Paul Vicol
Jeff Z. HaoChen
Roger C. Grosse
ODL
380
15
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28 Feb 2022
Supervising the Multi-Fidelity Race of Hyperparameter Configurations
Neural Information Processing Systems (NeurIPS), 2022
Martin Wistuba
Arlind Kadra
Josif Grabocka
335
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0
20 Feb 2022
Neural Architecture Search for Energy Efficient Always-on Audio Models
Daniel T. Speckhard
Karolis Misiunas
Sagi Perel
Tenghui Zhu
S. Carlile
M. Slaney
209
19
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09 Feb 2022
Learning Curves for Decision Making in Supervised Machine Learning: A Survey
Machine-mediated learning (ML), 2022
F. Mohr
Jan N. van Rijn
357
84
0
28 Jan 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
350
77
0
27 Dec 2021
Automated Deep Learning: Neural Architecture Search Is Not the End
Xuanyi Dong
D. Kedziora
Katarzyna Musial
Bogdan Gabrys
551
30
0
16 Dec 2021
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers
Julia Moosbauer
Martin Binder
Lennart Schneider
Florian Pfisterer
Marc Becker
Michel Lang
Lars Kotthoff
B. Bischl
185
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29 Nov 2021
Towards Green Automated Machine Learning: Status Quo and Future Directions
Journal of Artificial Intelligence Research (JAIR), 2021
Tanja Tornede
Alexander Tornede
Jonas Hanselle
Marcel Wever
F. Mohr
Eyke Hüllermeier
483
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10 Nov 2021
NAS-Bench-x11 and the Power of Learning Curves
Shen Yan
Colin White
Yash Savani
Katharina Eggensperger
234
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05 Nov 2021
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation
Ross M. Clarke
E. T. Oldewage
José Miguel Hernández-Lobato
442
9
0
20 Oct 2021
Genealogical Population-Based Training for Hyperparameter Optimization
Antoine Scardigli
P. Fournier
Matteo Vilucchio
D. Naccache
GP
159
0
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30 Sep 2021
Faster Improvement Rate Population Based Training
Valentin Dalibard
Max Jaderberg
179
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