2024
Advances and Challenges in Meta-Learning: A Technical Review
A. Vettoruzzo, M.-R. Bouguelia, J. Vanschoren, T. Rognvaldsson, and K. C. Santosh
IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)
Published
Can Fairness Be Automated? Guidelines and Opportunities for Fairness-Aware AutoML
H. Weerts, F. Pfisterer, M. Feurer, K. Eggensperger, E. Bergman, N. Awad, J. Vanschoren, M. Pechenizkiy, B. Bischl, and F. Hutter
Journal of Artificial Intelligence Research, 79, 639--677 (2024)
Published
Croissant: A Metadata Format for ML-Ready Datasets
M. Akhtar, O. Benjelloun, C. Conforti, P. Gijsbers, J. Giner-Miguelez, N. Jain, M. Kuchnik, Q. Lhoest, P. Marcenac, M. Maskey, P. Mattson, L. Oala, P. Ruyssen, R. Shinde, E. Simperl, G. Thomas, S. Tykhonov, J. Vanschoren, J. van der Velde, S. Vogler, and C.-J. Wu
Advances in Neural Information Processing Systems (NeurIPS 2024) (2024)
Published
TrustLLM: Trustworthiness in Large Language Models
Y. Huang, L. Sun, H. Wang, and others, and J. Vanschoren
International Conference on Machine Learning (ICML 2024), 20166--20270 (2024)
Published
Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates
M. O. Yildirim, E. C. Gok, G. Sokar, D. C. Mocanu, and J. Vanschoren
Conference on Parsimony and Learning (CPAL 2024), 94--107 (2024)
Published- Croissant: A Metadata Format for ML-Ready Datasets
M. Akhtar, O. Benjelloun, C. Conforti, P. Gijsbers, J. Giner-Miguelez, N. Jain, M. Kuchnik, Q. Lhoest, P. Marcenac, M. Maskey, P. Mattson, L. Oala, P. Ruyssen, R. Shinde, E. Simperl, G. Thomas, V. Tykhonov, J. Vanschoren, S. Vogler, and C.-J. Wu
SIGMOD/PODS Workshop on Data Management for End-to-End Machine Learning (DEEM 2024), 1--6 (2024)
Published - International Conference on Automated Machine Learning
M. Lindauer, K. Eggensperger, R. Garnett, and J. Vanschoren
Proceedings of Machine Learning Research, Volume 256, PMLR, 2024 (2024)
Published - A Standardized Machine-Readable Dataset Documentation Format for Responsible AI
N. Jain, M. Akhtar, J. Giner-Miguelez, R. Shinde, J. Vanschoren, S. Vogler, S. Goswami, Y. Rao, T. Santos, and L. Oala
arXiv preprint arXiv:2407.16883, 2024 (2024)
Preprint - Automatic Combination of Sample Selection Strategies for Few-Shot Learning
B. Pecher, I. Srba, M. Bielikova, and J. Vanschoren
arXiv preprint arXiv:2402.03038, 2024 (2024)
Preprint - CLAMS: A System for Zero-Shot Model Selection for Clustering
P. Singh, P. Gijsbers, M. O. Yildirim, E. C. Gok, and J. Vanschoren
arXiv preprint arXiv:2407.11286, 2024 (2024)
Preprint - Robust and Efficient Transfer Learning via Supernet Transfer in Warm-Started Neural Architecture Search
P. Singh and J. Vanschoren
arXiv preprint arXiv:2407.20279, 2024 (2024)
Preprint - Can Time Series Forecasting Be Automated? A Benchmark and Analysis
A. T. Sreedhara and J. Vanschoren
arXiv preprint arXiv:2407.16445, 2024 (2024)
Preprint - Unsupervised Meta-Learning via In-Context Learning
A. Vettoruzzo, L. Braccaioli, J. Vanschoren, and M. Nowaczyk
arXiv preprint arXiv:2405.16124, 2024 (2024)
Preprint - Introducing v0.5 of the AI Safety Benchmark from MLCommons
B. Vidgen, A. Agrawal, A. M. Ahmed, V. Akinwande, N. Al-Nuaimi, N. Alfaraj, E. Alhajjar, L. Aroyo, T. Bavalatti, B. Blili-Hamelin, and J. Vanschoren
arXiv preprint arXiv:2404.12241, 2024 (2024)
Preprint 
Continual Learning on a Data Diet
E. C. Gok Yildirim, M. O. Yildirim, and J. Vanschoren
arXiv preprint arXiv:2410.17715, 2024 (2024)
Preprint
FOCIL: Finetune-and-Freeze for Online Class Incremental Learning by Training Randomly Pruned Sparse Experts
M. O. Yildirim, E. C. Gok Yildirim, D. C. Mocanu, and J. Vanschoren
arXiv preprint arXiv:2403.14684, 2024 (2024)
Preprint
2023
Signal Quality Analysis for Long-Term ECG Monitoring Using a Health Patch in Cardiac Patients
I. Campero Jurado, I. Lorato, J. Morales, L. Fruytier, S. Stuart, P. Panditha, D. M. Janssen, N. Rossetti, N. Uzunbajakava, I. B. Serban, L. Rikken, M. de Kok, J. Vanschoren, and A. Brombacher
Sensors, 23 (4), Art. 2130 (2023)
Published- Online AutoML: An Adaptive AutoML Framework for Online Learning
B. Celik, P. Singh, and J. Vanschoren
Machine Learning, 112 (6), 1897--1921 (2023)
Published 
Automated Machine Learning Approach in Material Discovery of Hole Selective Layers for Perovskite Solar Cells
M. O. Yildirim, E. C. Gok Yildirim, E. Eren, P. Huang, M. P. U. Haris, S. Kazim, J. Vanschoren, A. Uygun Oksuz, and S. Ahmad
Energy Technology, 11 (1) (2023)
Published- Efficient-DASH: Automated Radar Neural Network Design Across Tasks and Datasets
T. Boot, N. Cazin, W. Sanberg, and J. Vanschoren
IEEE Intelligent Vehicles Symposium (IV 2023), 1--7 (2023)
Published 
An Analysis of Evolutionary Migration Models for Multi-Objective, Multi-Fidelity AutoML
I. Campero-Jurado and J. Vanschoren
IEEE International Conference on Systems, Man, and Cybernetics (SMC 2023), 2940--2945 (2023)
Published- Neural Architecture Search for Visual Anomaly Segmentation
T. Kerssies and J. Vanschoren
AutoML Conference (AutoML 2023) (2023)
Published - Are Labels Needed for Incremental Instance Learning?
M. Kilickaya and J. Vanschoren
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), 2401--2409 (2023)
Published - Dataperf: Benchmarks for Data-Centric AI Development
M. Mazumder, C. Banbury, X. Yao, and others, and J. Vanschoren
Advances in Neural Information Processing Systems (NeurIPS 2023) (2023)
Published - AutoML for Outlier Detection with Optimal Transport Distances
P. Singh and J. Vanschoren
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023), 7175--7178 (2023)
Published 
AdaCL: Adaptive Continual Learning
E. C. Gok Yildirim, M. O. Yildirim, M. Kilickaya, and J. Vanschoren
Continual AI Unconference (ContinualAI 2024), PMLR, 249, 15--24 (2023)
Published- Locality-Aware Hyperspectral Classification
F. Zhou, M. Kilickaya, and J. Vanschoren
The British Machine Vision Conference (BMVC 2023) (2023)
Published - NeurIPS’22 Cross-Domain MetaDL Challenge: Results and Lessons Learned
D. Carrión-Ojeda, M. Alam, S. Escalera, A. Farahat, D. Ghosh, T. G. Diaz, C. Gupta, I. Guyon, J. R. Ky, X. Y. Lee, X. Liu, F. Mohr, M. H. Nguyen, E. Pintelas, S. Roth, S. Schaub-Meyer, H. Sun, I. Ullah, J. Vanschoren, L. Vidyaratne, J. Wu, and X. Yin
NeurIPS 2022 Competition Track, 50--72 (2023)
Published - Democratising Artificial Intelligence to Accelerate Scientific Discovery
J. Vanschoren
In: Artificial Intelligence in Science, OECD, 2023 (2023)
Published - Evaluating Continual Test-Time Adaptation for Contextual and Semantic Domain Shifts
T. Kerssies, M. Kılıçkaya, and J. Vanschoren
arXiv preprint arXiv:2208.08767, 2023 (2023)
Preprint - What Can AutoML Do for Continual Learning?
M. Kılıçkaya and J. Vanschoren
arXiv preprint arXiv:2311.11963, 2023 (2023)
Preprint - DMLR: Data-Centric Machine Learning Research -- Past, Present and Future
L. Oala, M. Maskey, L. Bat-Leah, A. Parrish, N. M. Gürel, T.-S. Kuo, Y. Liu, R. Dror, D. Brajovic, X. Yao, and J. Vanschoren
arXiv preprint arXiv:2311.13028, 2023 (2023)
Preprint
2022
- AgroML: An Open-Source Repository to Forecast Reference Evapotranspiration in Different Geo-Climatic Conditions Using Machine Learning and Transformer-Based Models
J. A. Bellido-Jiménez, J. Estévez, J. Vanschoren, and A. P. García-Marín
Agronomy, 12 (3), 656 (2022)
Published 
Interpretable Assessment of ST-Segment Deviation in ECG Time Series
I. Campero Jurado, A. Fedjajevs, J. Vanschoren, and A. Brombacher
Sensors, 22 (13), Art. 4919 (2022)
Published- Meta-Features for Meta-Learning
A. Rivolli, L. P. F. Garcia, C. Soares, J. Vanschoren, and A. C. P. L. F. de Carvalho
Knowledge-Based Systems, 240, 108101 (2022)
Published - Theory-Based Habit Modeling for Enhancing Behavior Prediction in Behavior Change Support Systems
C. Zhang, J. Vanschoren, A. van Wissen, D. Lakens, B. de Ruyter, and W. A. IJsselsteijn
User Modeling and User-Adapted Interaction, 23 (2022)
Published 
Multi-Fidelity Optimization Method with Asynchronous Generalized Island Model for AutoML
I. Campero-Jurado and J. Vanschoren
Genetic and Evolutionary Computation Conference (GECCO 2022) (2022)
Published- Meta-Album: Multi-Domain Meta-Dataset for Few-Shot Image Classification
I. Ullah, D. Carrión-Ojeda, S. Escalera, I. Guyon, M. Huisman, F. Mohr, J. N. van Rijn, H. Sun, J. Vanschoren, and P. A. Vu
Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 3232--3247 (2022)
Published - Regularized Meta-Learning for Neural Architecture Search
R. van Gastel and J. Vanschoren
Automated Machine Learning Conference (AutoML 2022) (2022)
Published - Faster Performance Estimation for NAS with Embedding Proximity Score
G. Franken, P. Singh, and J. Vanschoren
ECMLPKDD Workshop on Meta-Knowledge Transfer, PMLR, 51--61 (2022)
Published - Introduction to the Special Section on AI in Manufacturing: Current Trends and Challenges
J. Lijffijt, D. Gkorou, P. Van Hertum, A. Ypma, M. Pechenizkiy, and J. Vanschoren
ACM SIGKDD Explorations Newsletter, 24 (2), 81--85 (2022)
Published - Metalearning: Applications to Automated Machine Learning and Data Mining
P. Brazdil, J. N. van Rijn, C. Soares, and J. Vanschoren
Springer Nature, 2022 (2022)
Published - Automated Reinforcement Learning: An Overview
R. R. Afshar, Y. Zhang, J. Vanschoren, and U. Kaymak
arXiv preprint arXiv:2201.05000, 2022 (2022)
Preprint - Warm-starting DARTS Using Meta-Learning
M. Grobelnik and J. Vanschoren
arXiv preprint arXiv:2205.06355, 2022 (2022)
Preprint
2021
- A Comparison of Optimisation Algorithms for High-Dimensional Particle and Astrophysics Applications
C. Balázs, M. van Beekveld, S. Caron, B. M. Dillon, B. Farmer, A. Fowlie, W. Handley, L. Hendriks, G. Jóhannesson, A. Leinweber, J. Mamužić, G. D. Martinez, P. Scott, E. C. Garrido-Merchán, R. Ruiz de Austri, Z. Searle, B. Stienen, J. Vanschoren, and M. White
Journal of High Energy Physics, 2021 (5), 1–46 (2021)
Published - Adaptation Strategies for Automated Machine Learning on Evolving Data
B. Celik and J. Vanschoren
IEEE Transactions on Pattern Analysis and Machine Intelligence, 43 (9) (2021)
Published - Transformational Machine Learning: Learning How to Learn from Many Related Scientific Problems
I. Olier, I. O. Oghenejokpeme, T. Dash, A. Davis, L. N. Soldatova, J. Vanschoren, and R. D. King
Proceedings of the National Academy of Sciences (PNAS), 118 (49) (2021)
Published - OpenML Benchmarking Suites
B. Bischl, G. Casalicchio, M. Feurer, F. Hutter, M. Lang, R. G. Mantovani, J. N. van Rijn, and J. Vanschoren
Proceedings of the NeurIPS Track on Datasets and Benchmarks 2021 (2021)
Published - Advances in MetaDL: AAAI 2021 Challenge and Workshop
A. E. Baz, I. Guyon, Z. Liu, J. van Rijn, S. Treguer, and J. Vanschoren
AAAI 2021 Workshop on Meta-Learning and MetaDL, PMLR 140:1--16 (2021)
Published - Variational Task Encoders for Model-Agnostic Meta-Learning with Uncertainty Over Task Distributions
L. Schragen and J. Vanschoren
Workshop on Meta-Learning @ NeurIPS 2021 (2021)
Published - From Strings to Data Science: A Practical Framework for Automated String Handling
J. van Lith and J. Vanschoren
Workshop on Automated Data Science @ ECMLPKDD 2021 (2021)
Published - Open-Ended Learning Strategies for Learning Complex Locomotion Skills
F. Zhou and J. Vanschoren
Workshop on Meta-Learning @ NeurIPS 2021 (2021)
Published - Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks
J. Vanschoren and S. Yeung
NeurIPS Foundation, Curran Associates, 2021 (2021)
Published - Proceedings of the AAAI 2021 Workshop on Meta-Learning and MetaDL Challenge
I. Guyon, J. N. van Rijn, S. Treguer, and J. Vanschoren
PMLR, 2021 (2021)
Published - Automated Feature Selection and Classification for High-Dimensional Biomedical Data
T. P. Beishuizen, J. Vanschoren, P. A. Hilbers, and D. Bošnački
ResearchSquare, 2021 (2021)
Published - Cats, Not CAT Scans: A Study of Dataset Similarity in Transfer Learning for 2D Medical Image Classification
I. van den Brandt, F. Fok, B. Mulders, J. Vanschoren, and V. Cheplygina
arXiv preprint arXiv:2107.05940, 2021 (2021)
Preprint - Frugal Machine Learning
M. Evchenko, J. Vanschoren, H. H. Hoos, M. Schoenauer, and M. Sebag
arXiv preprint arXiv:2111.03731, 2021 (2021)
Preprint - Fixed-Point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms
R. Goyal, J. Vanschoren, V. Van Acht, and S. Nijssen
arXiv preprint arXiv:2102.02147, 2021 (2021)
Preprint
2020
- Aerial Imagery Pixel-Level Segmentation
M. R. Heffels and J. Vanschoren
arXiv preprint arXiv:2012.02024, 2020 (2020)
Preprint - Importance of Tuning Hyperparameters of Machine Learning Algorithms
H. Weerts, A. Mueller, and J. Vanschoren
arXiv preprint arXiv:2007.07588, 2020 (2020)
Preprint
2019
- A Meta-Learning Recommender System for Hyperparameter Tuning: Predicting When Tuning Improves SVM Classifiers
R. G. Mantovani, A. L. D. Rossi, E. Alcobaca, J. Vanschoren, and A. C. P. L. F. Carvalho
Information Sciences, 501, 193–221 (2019)
Published - Multi-Task Learning with a Natural Metric for Quantitative Structure Activity Relationship Learning
N. Sadawi, I. Olier, J. Vanschoren, J. N. van Rijn, J. Besnard, R. Bickerton, C. Grosan, L. Soldatova, and R. D. King
Journal of Cheminformatics, 11 (1), Art. 68 (2019)
Published - Beyond Bag-of-Concepts: Vectors of Locally Aggregated Concepts
M. Grootendorst and J. Vanschoren
Proceedings of ECMLPKDD 2019 (2019)
Published - The ABC of Data: A Classifying Framework for Data Readiness
L. A. Castelijns, Y. Maas, and J. Vanschoren
Workshop on Automating Data Science @ ECMLPKDD 2019 (2019)
Published - Learning to Go with the Flow: On the Adaptability of Automated Machine Learning to Evolving Data
B. Celik and J. Vanschoren
Workshop on Automating Data Science @ ECMLPKDD 2019 (2019)
Published - An Open Source AutoML Benchmark
P. Gijsbers, E. Ledell, J. Thomas, S. Poirier, B. Bischl, and J. Vanschoren
Automated Machine Learning Workshop @ ICML 2019 (2019)
Published - Meta-Learning for Algorithm and Hyperparameter Optimization with Surrogate Model Ensembles
G. Manolache and J. Vanschoren
Meta-Learning Workshop @ NeurIPS 2019 (2019)
Published - Learning to Reinforcement Learn for Neural Architecture Search
J. Robles and J. Vanschoren
New in ML Symposium @ NeurIPS 2019 (2019)
Published - HyperBoost: Hyperparameter Optimization by Gradient Boosting Surrogate Models
J. van Hoof and J. Vanschoren
Workshop on Automating Data Science @ ECMLPKDD 2019 (2019)
Published - Meta-Learning
J. Vanschoren
In: Automatic Machine Learning: Methods, Systems, Challenges. Springer, 2019 (2019)
Published - Automatic Machine Learning: Methods, Systems, Challenges
F. Hutter, L. Kotthoff, and J. Vanschoren
Springer, 2019 (2019)
Published - MLSys: The New Frontier of Machine Learning Systems
A. Ratner, J. Vanschoren, and and others
arXiv preprint arXiv:1904.03257, 2019 (2019)
Preprint
2018
- Speeding Up Algorithm Selection via Meta-Learning and Active Testing
S. Abdulrahman, P. Brazdil, J. N. van Rijn, and J. Vanschoren
Machine Learning, 107 (1), 79–108 (2018)
Published - Meta-QSAR: Learning How to Learn QSARs
I. Olier, N. Sadawi, G. R. Bickerton, J. Vanschoren, C. Grosan, L. Soldatova, and R. D. King
Machine Learning, 107 (1), 285–311 (2018)
Published - The Online Performance Estimation Framework: Heterogeneous Ensemble Learning for Data Streams
J. N. van Rijn, G. Holmes, B. Pfahringer, and J. Vanschoren
Machine Learning, 107 (1), 149–176 (2018)
Published - ML Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies
G. Correa Publio, D. Esteves, A. Ławrynowicz, P. Panov, L. Soldatova, T. Soru, J. Vanschoren, and H. Zafar
ICML 2018 Workshop on Reproducibility in Machine Learning (2018)
Published - Meta Learning for Defaults: Symbolic Defaults
J. N. van Rijn, F. Pfisterer, J. Thomas, A. Mueller, B. Bischl, and J. Vanschoren
Meta-Learning Workshop @ NeurIPS 2018 (2018)
Published - Data Augmentation Using Conditional Generative Adversarial Networks for Leaf Counting in Arabidopsis Plants
Y. Zhu, M. Aoun, M. Krijn, and J. Vanschoren
CCCPV Workshop @ BMVC 2018 (2018)
Published - Proceedings of the 21st International Conference on Discovery Science
L. Soldatova, J. Vanschoren, G. Papadopoulos, and M. Ceci
Lecture Notes in Artificial Intelligence 11198, DS 2018 (2018)
Published - Metalearning: A Survey
J. Vanschoren
arXiv preprint arXiv:1810.03548, 2018 (2018)
Preprint - Towards Reproducible Empirical Research in Meta-Learning
A. Rivolli, L. Garcia, C. Soares, J. Vanschoren, and A. C. de Carvalho
arXiv preprint arXiv:1808.10406, 2018 (2018)
Preprint
2017
- OpenML: An R Package to Connect to the Networked Machine Learning Platform
G. Casalicchio, B. Hofner, M. Lang, D. Kirchhoff, P. Kerschke, H. Seibold, J. Bossek, J. Vanschoren, and B. Bischl
Computational Statistics, 32 (3), 1–15 (2017)
Published 
Layered TPOT: Speeding Up Tree-Based Pipeline Optimization
P. Gijsbers, J. Vanschoren, and R. Olson
AutoML Workshop @ ECML 2017, CEUR Workshop Proceedings vol. 1998 (2017)
Published- Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning
W. Duivesteijn, M. Pechenizkiy, G. H. L. Fletcher, V. Menkovski, E. J. Postma, and J. Vanschoren
Eindhoven University of Technology Eindhoven, 2017 (2017)
Published
2016
- An Algorithm, Implementation and Execution Ontology Design Pattern
A. Lawrynowicz, D. Esteves, P. Panov, T. Soru, S. Dzeroski, and J. Vanschoren
In: Studies on the Semantic Web (Hitzler.P., Gangemi, A., Janowicz, K., Krisnadhi, A., Presutti, V., eds.), IOS Press (2016)
Published - ASlib: A Benchmark Library for Algorithm Selection
B. Bischl, P. Kerschke, L. Kotthoff, M. Lindauer, Y. Malitsky, A. Frechette, H. Hoos, F. Hutter, K. Leyton-Brown, K. Tierney, and J. Vanschoren
Artificial Intelligence, 237, 41–58 (2016)
Published - Reduction of False Arrhythmia Alarms Using Signal Selection and Machine Learning
L. M. Eerikainen, J. Vanschoren, M. J. Rooijakkers, R. Vullings, and R. M. Aarts
Physiological Measurement, 37 (8), 1204–1216 (2016)
Published - Towards Understanding Online Sentiment Expression: An Interdisciplinary Approach with Subgroup Comparison and Visualization
B. Gao, B. Berendt, and J. Vanschoren
Social Network Analysis and Mining, 6 (1), 68:1–68:16 (2016)
Published - Connecting R to the OpenML Project for Open Machine Learning
B. Bischl, G. Casalicchio, B. Hofner, P. Kerschke, D. Kirchhoff, M. Lang, H. Seibold, and J. Vanschoren
UseR! Conference (UseR 2016), 1--11 (2016)
Published - Anticipating Habit Formation: A Psychological Computing Approach to Behavior Change Support
C. Zhang, A. van Wissen, D. Lakens, J. Vanschoren, B. De Ruyter, and W. A. IJsselsteijn
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016), 1247--1254 (2016)
Published - Hyper-Parameter Tuning of a Decision Tree Induction Algorithm
R. G. Mantovani, T. Horvath, R. Cerri, A. P. L. F. Carvalho, and J. Vanschoren
Brazilian Conference on Intelligent Systems (BRACIS 2016) (2016)
Published - Proceedings of the 10th International Conference on Learning and Intelligent Optimization
P. Festa, M. Sellmann, and J. Vanschoren
Lecture Notes in Computer Science 10079, LION 2016 (2016)
Published - Proceedings of the ICML 2016 Workshop on Automatic Machine Learning
F. Hutter, L. Kotthoff, and J. Vanschoren
PMLR, 2016 (2016)
Published
2015
- Decreasing the False Alarm Rate of Arrhythmias in Intensive Care Using a Machine Learning Approach
L. M. Eerikainen, J. Vanschoren, M. J. Rooijakkers, R. Vullings, and R. M. Aarts
IEEE Computing in Cardiology, 42, 293-297 (2015)
Published - Who is More Positive in Private? Analyzing Sentiment Differences Across Privacy Levels and Demographic Factors in Facebook Chats and Posts
B. Gao, B. Berendt, and J. Vanschoren
IEEE/ACM Proceedings of ASONAM 2015, 605-610 (2015)
Published - To Tune or Not to Tune: Recommending When to Adjust SVM Hyper-Parameters via Meta-Learning
R. G. Mantovani, A. L. D. Rossi, J. Vanschoren, B. Bischl, and A. C. P. L. F. Carvalho
IEEE Proceedings of IJCNN 2015, 1-8 (2015)
Published - Effectiveness of Random Search in SVM Hyper-Parameter Tuning
R. G. Mantovani, A. L. D. Rossi, J. Vanschoren, B. Bischl, and A. C. P. L. F. Carvalho
IEEE Proceedings of IJCNN 2015, 1-8 (2015)
Published - Fast Algorithm Selection Using Learning Curves
J. N. van Rijn, S. M. Abdulrahman, P. Brazdil, and J. Vanschoren
Advances in Intelligent Data Analysis XIV (IDA 2015), Lecture Notes in Computer Science 9385, 298-309 (2015)
Published - Towards a Data Science Collaboratory
J. Vanschoren, B. Bischl, F. Hutter, M. Sebag, B. Kegl, M. Schmid, G. Napolitano, K. Wolstencroft, A. R. Williams, and N. Lawrence
Advances in Intelligent Data Analysis XIV (IDA 2015), Lecture Notes in Computer Science 9385, XIX-XXI (2015)
Published - Algorithm Selection via Meta-Learning and Sample-Based Active Testing
S. Abdulrahman, P. Brazdil, J. N. van Rijn, and J. Vanschoren
MetaSel Workshop @ PKDD/ECML 2015, CEUR Workshop Proceedings 1455, 55-66 (2015)
Published - Meta-Learning Recommendation of Default Hyper-Parameter Values for SVMs in Classification Tasks
R. G. Mantovani, A. L. D. Rossi, J. Vanschoren, and A. C. P. L. F. Carvalho
MetaSel Workshop @ PKDD/ECML 2015, CEUR Workshop Proceedings 1455, 80-92 (2015)
Published - Sharing RapidMiner Workflows and Experiments with OpenML
J. N. van Rijn and J. Vanschoren
MetaSel Workshop @ PKDD/ECML 2015, CEUR Workshop Proceedings 1455, 93-103 (2015)
Published - Taking Machine Learning Research Online with OpenML
J. Vanschoren, J. N. van Rijn, and B. Bischl
JMLR Workshop and Conference Proceedings (BigMine 2015), 41, 1-4 (2015)
Published - Towards a Collaborative Platform for Advanced Meta-Learning in Healthcare Predictive Analytics
M. Vukicevic, S. Radovanovic, J. Vanschoren, G. Napolitano, and B. Delibasic
MetaSel Workshop @ PKDD/ECML 2015, CEUR Workshop Proceedings 1455, 112-114 (2015)
Published - Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection @ ECMLPKDD
J. Vanschoren, P. Brazdil, C. G. Giraud-Carrier, and L. Kotthoff
CEUR Workshop Proceedings 1455, CEUR 2015 (2015)
Published
2014
- OpenML: Networked Science in Machine Learning
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luis Torgo
ACM SIGKDD Explorations Newsletter (2014)
Preprint Github Published - Towards Meta-Learning on Data Streams
J. N. van Rijn, G. Holmes, B. Pfahringer, and J. Vanschoren
MetaSel Workshop @ ECAI 2014, CEUR Workshop Proceedings 1201, 37-38 (2014)
Published - Algorithm Selection on Data Streams
J. N. van Rijn, G. Holmes, B. Pfahringer, and J. Vanschoren
Proceedings of Discovery Science 2014, Lecture Notes in Computer Science 8777, 325-336 (2014)
Published - Proceedings of the 2014 International Workshop on Meta-Learning and Algorithm Selection @ ECAI
J. Vanschoren, P. Brazdil, and L. Kotthoff
CEUR Workshop Proceedings 1201, CEUR 2014 (2014)
Published - Reconstructing Medieval Social Networks from English and Latin Charters
A. J. Knobbe, M. Meeng, J. Vanschoren, S. Rees Jones, and S. Merlo Penning
Population Reconstruction 2014 (2014)
Published
2013
- A Survey of Intelligent Assistants for Data Analysis
F. Serban, J. Vanschoren, J. U. Kietz, and A. Bernstein
ACM Computing Surveys, 45 (3), Art. 31 (2013)
Published - A RapidMiner Extension for Open Machine Learning
J. N. van Rijn, V. Umaashankar, S. Fischer, B. Bischl, T. Lorgo, B. Gao, P. Winter, B. Wiswedel, M. R. Berthold, and J. Vanschoren
Proceedings of RCOMM 2013, 59-70 (2013)
Published
2012
- Experiment Databases: A New Way to Share, Organize and Learn from Experiments
J. Vanschoren, H. Blockeel, B. Pfahringer, and G. Holmes
Machine Learning, 87 (2), 127–158 (2012)
Published