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Learning Curves for Decision Making in Supervised Machine Learning: A Survey
Machine-mediated learning (ML), 2022
28 January 2022
F. Mohr
Jan N. van Rijn
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Papers citing
"Learning Curves for Decision Making in Supervised Machine Learning: A Survey"
43 / 43 papers shown
Zero-Shot Performance Prediction for Probabilistic Scaling Laws
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Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
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Dong Bok Lee
Steven Adriaensen
Juho Lee
Sung Ju Hwang
Frank Hutter
Seon Joo Kim
Hae Beom Lee
BDL
449
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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
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21 May 2025
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
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10 Oct 2024
MD tree: a model-diagnostic tree grown on loss landscape
Yefan Zhou
Jianlong Chen
Qinxue Cao
Konstantin Schürholt
Yaoqing Yang
400
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24 Jun 2024
Unraveling overoptimism and publication bias in ML-driven science
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Pouria Saidi
Gautam Dasarathy
Visar Berisha
338
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23 May 2024
AI Competitions and Benchmarks: Dataset Development
Romain Egele
Julio C. S. Jacques Junior
Jan N. van Rijn
Isabelle M Guyon
Xavier Baró
Albert Clapés
Dali Wang
Sergio Escalera
T. Moeslund
Jun Wan
217
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0
15 Apr 2024
The Unreasonable Effectiveness Of Early Discarding After One Epoch In Neural Network Hyperparameter Optimization
Romain Egele
Felix Mohr
Tom Viering
Dali Wang
306
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05 Apr 2024
Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting
Hao Li
Gopi Krishnan Rajbahadur
Dayi Lin
Cor-Paul Bezemer
Zhen Ming Jiang
Jiang
308
68
0
18 Jan 2024
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
Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning
AAAI Conference on Artificial Intelligence (AAAI), 2023
Joseph Giovanelli
Alexander Tornede
Tanja Tornede
Marius Lindauer
455
8
0
07 Sep 2023
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?
The European Symposium on Artificial Neural Networks (ESANN), 2023
Romain Egele
Isabelle M Guyon
Yixuan Sun
Dali Wang
338
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0
28 Jul 2023
Multi-Fidelity Multi-Armed Bandits Revisited
Neural Information Processing Systems (NeurIPS), 2023
Xuchuang Wang
Qingyun Wu
Wei Chen
John C. S. Lui
165
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13 Jun 2023
Artificial intelligence to advance Earth observation: a perspective
IEEE Geoscience and Remote Sensing Magazine (GRSM), 2023
D. Tuia
Konrad Schindler
Begüm Demir
Gustau Camps-Valls
Xiao Xiang Zhu
...
Mihai Datcu
Jorge-Arnulfo Quiané-Ruiz
Volker Markl
Bertrand Le Saux
Rochelle Schneider
385
33
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15 May 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
Scaling Laws for Hyperparameter Optimization
Neural Information Processing Systems (NeurIPS), 2023
Arlind Kadra
Maciej Janowski
Martin Wistuba
Josif Grabocka
554
22
0
01 Feb 2023
Learning to Rank Normalized Entropy Curves with Differentiable Window Transformation
Hanyang Liu
Shuai Yang
Feng Qi
Shuaiwen Wang
353
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25 Jan 2023
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance
Marco Loog
T. Viering
211
2
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25 Nov 2022
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
PASHA: Efficient HPO and NAS with Progressive Resource Allocation
International Conference on Learning Representations (ICLR), 2022
Ondrej Bohdal
Lukas Balles
Martin Wistuba
Beyza Ermis
Cédric Archambeau
Giovanni Zappella
343
14
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14 Jul 2022
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets
IFIP Working Conference on Database Semantics (IWDS), 2022
Charles Moussa
Jan N. van Rijn
Thomas Bäck
Vedran Dunjko
291
13
0
20 Jun 2022
Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview
ACM Transactions on Evolutionary Learning and Optimization (TELO), 2022
Florian Karl
Tobias Pielok
Julia Moosbauer
Florian Pfisterer
Stefan Coors
...
Jakob Richter
Michel Lang
Eduardo C. Garrido-Merchán
Juergen Branke
J. Herbinger
AI4CE
458
102
0
15 Jun 2022
Towards Meta-learned Algorithm Selection using Implicit Fidelity Information
Aditya Mohan
Tim Ruhkopf
Marius Lindauer
FedML
236
4
0
07 Jun 2022
Fast and Informative Model Selection using Learning Curve Cross-Validation
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
F. Mohr
Jan N. van Rijn
172
54
0
27 Nov 2021
The Shape of Learning Curves: a Review
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
T. Viering
Marco Loog
397
200
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19 Mar 2021
Recommending Training Set Sizes for Classification
Phillip T. Koshute
Jared Zook
I. Mcculloh
305
7
0
16 Feb 2021
Risk-Monotonicity in Statistical Learning
Neural Information Processing Systems (NeurIPS), 2020
Zakaria Mhammedi
662
9
0
28 Nov 2020
Small Data, Big Decisions: Model Selection in the Small-Data Regime
International Conference on Machine Learning (ICML), 2020
J. Bornschein
Francesco Visin
Simon Osindero
190
48
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26 Sep 2020
Optimal Regularization Can Mitigate Double Descent
International Conference on Learning Representations (ICLR), 2020
Preetum Nakkiran
Prayaag Venkat
Sham Kakade
Tengyu Ma
448
148
0
04 Mar 2020
Deep Double Descent: Where Bigger Models and More Data Hurt
International Conference on Learning Representations (ICLR), 2019
Preetum Nakkiran
Gal Kaplun
Yamini Bansal
Tristan Yang
Boaz Barak
Ilya Sutskever
583
1,096
0
04 Dec 2019
Making Learners (More) Monotone
International Symposium on Intelligent Data Analysis (IDA), 2019
T. Viering
A. Mey
Marco Loog
258
11
0
25 Nov 2019
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
Machine-mediated learning (ML), 2019
Eyke Hüllermeier
Willem Waegeman
PER
UD
953
1,913
0
21 Oct 2019
Minimizers of the Empirical Risk and Risk Monotonicity
Neural Information Processing Systems (NeurIPS), 2019
Marco Loog
T. Viering
A. Mey
384
30
0
11 Jul 2019
Inductive Transfer for Neural Architecture Optimization
Martin Wistuba
Tejaswini Pedapati
230
9
0
08 Mar 2019
Progressive Sampling-Based Bayesian Optimization for Efficient and Automatic Machine Learning Model Selection
Xueqiang Zeng
G. Luo
144
78
0
06 Dec 2018
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Aaron Klein
Stefan Falkner
Simon Bartels
Philipp Hennig
Katharina Eggensperger
AI4CE
338
601
0
23 May 2016
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
Lisha Li
Kevin Jamieson
Giulia DeSalvo
Afshin Rostamizadeh
Ameet Talwalkar
663
2,724
0
21 Mar 2016
Selecting Near-Optimal Learners via Incremental Data Allocation
Ashish Sabharwal
Horst Samulowitz
Gerald Tesauro
248
64
0
31 Dec 2015
How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?
Junghwan Cho
Kyewook Lee
Ellie Shin
G. Choy
Synho Do
359
373
0
19 Nov 2015
Non-stochastic Best Arm Identification and Hyperparameter Optimization
Kevin Jamieson
Ameet Talwalkar
436
666
0
27 Feb 2015
Freeze-Thaw Bayesian Optimization
Kevin Swersky
Jasper Snoek
Ryan P. Adams
456
286
0
16 Jun 2014
Sample Size Planning for Classification Models
Analytica Chimica Acta (Anal. Chim. Acta), 2012
C. Beleites
U. Neugebauer
T. Bocklitz
C. Krafft
J. Popp
385
446
0
06 Nov 2012
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction
Journal of Artificial Intelligence Research (JAIR), 2003
F. Provost
Gary M. Weiss
281
1,012
0
22 Jun 2011
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