Investigating Classifier Learning Behavior with Experiment Databases. - Joaquin Vanschoren; Hendrik Blockeel (GfKl, 2007)

Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning. - Hendrik Blockeel; Joaquin Vanschoren (PKDD, 2007)

Organizing the World's Machine Learning Information. - Joaquin Vanschoren; Hendrik Blockeel; Bernhard Pfahringer; Geoffrey Holmes 001 (ISoLA, 2008)

Learning from the Past with Experiment Databases. - Joaquin Vanschoren; Bernhard Pfahringer; Geoffrey Holmes 001 (PRICAI, 2008)

A Community-Based Platform for Machine Learning Experimentation. - Joaquin Vanschoren; Hendrik Blockeel (ECML/PKDD (2), 2009)

Experiment Databases. - Joaquin Vanschoren; Hendrik Blockeel (Inductive Databases and Constraint-Based Data Mining, 2010)

Understanding Machine Learning Performance with Experiment Databases (Het verwerven van inzichten in leerperformantie met experiment databanken) ; Understanding Machine Learning Performance with Experiment Databases. - Joaquin Vanschoren (2010)

Datenanalyse und -visualisierung. - Bettina Berendt; Joaquin Vanschoren; Bo Gao 002 (Handbuch Forschungsdatenmanagement, 2011)

Traffic Events Modeling for Structural Health Monitoring. - Ugo Vespier; Arno J. Knobbe; Joaquin Vanschoren; Shengfa Miao; Arne Koopman; Bas Obladen; Carlos Bosma (IDA, 2011)

Meta-Learning Architectures: Collecting, Organizing and Exploiting Meta-Knowledge. - Joaquin Vanschoren (Meta-Learning in Computational Intelligence, 2011)

Selecting Classification Algorithms with Active Testing. - Rui Leite; Pavel Brazdil; Joaquin Vanschoren (MLDM, 2012)

Scientific Workflow Management with ADAMS. - Peter Reutemann; Joaquin Vanschoren (ECML/PKDD (2), 2012)

MDL-Based Analysis of Time Series at Multiple Time-Scales. - Ugo Vespier; Arno J. Knobbe; Siegfried Nijssen; Joaquin Vanschoren (ECML/PKDD (2), 2012)

Experiment databases - A new way to share, organize and learn from experiments. - Joaquin Vanschoren; Hendrik Blockeel; Bernhard Pfahringer; Geoffrey Holmes 001 (Mach. Learn., 2012)

OpenML: A Collaborative Science Platform. - Jan N. van Rijn; Bernd Bischl; Luís Torgo; Bo Gao 002; Venkatesh Umaashankar; Simon Fischer 001; Patrick Winter; Bernd Wiswedel; Michael R. Berthold; Joaquin Vanschoren (ECML/PKDD (3), 2013)

A survey of intelligent assistants for data analysis. - Floarea Serban; Joaquin Vanschoren; Jörg-Uwe Kietz; Abraham Bernstein (ACM Comput. Surv., 2013)

OpenML: networked science in machine learning. - Joaquin Vanschoren; Jan N. van Rijn; Bernd Bischl; Luís Torgo (SIGKDD Explor., 2013)

Algorithm Selection on Data Streams. - Jan N. van Rijn; Geoffrey Holmes 001; Bernhard Pfahringer; Joaquin Vanschoren (Discovery Science, 2014)

Towards Meta-learning over Data Streams. - Jan N. van Rijn; Geoffrey Holmes 001; Bernhard Pfahringer; Joaquin Vanschoren (MetaSel@ECAI, 2014)

Open science in machine learning. - Joaquin Vanschoren; Mikio L. Braun; Cheng Soon Ong (CoRR, 2014)

Who is More Positive in Private? Analyzing Sentiment Differences across Privacy Levels and Demographic Factors in Facebook Chats and Posts. - Bo Gao 002; Bettina Berendt; Joaquin Vanschoren (ASONAM, 2015)

Decreasing the False Alarm Rate of Arrhythmias in Intensive Care Using a Machine Learning Approach. - Linda M. Eerikäinen; Joaquin Vanschoren; Michael Johannes Rooijakkers; Rik Vullings; Ronald M. Aarts (CinC, 2015)

Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams. - Jan N. van Rijn; Geoffrey Holmes 001; Bernhard Pfahringer; Joaquin Vanschoren (ICDM, 2015)

Fast Algorithm Selection Using Learning Curves. - Jan N. van Rijn; Salisu Mamman Abdulrahman; Pavel Brazdil; Joaquin Vanschoren (IDA, 2015)

To tune or not to tune: Recommending when to adjust SVM hyper-parameters via meta-learning. - Rafael Gomes Mantovani; André Luis Debiaso Rossi; Joaquin Vanschoren; Bernd Bischl; André C. P. L. F. de Carvalho (IJCNN, 2015)

Effectiveness of Random Search in SVM hyper-parameter tuning. - Rafael Gomes Mantovani; André Luis Debiaso Rossi; Joaquin Vanschoren; Bernd Bischl; André C. P. L. F. de Carvalho (IJCNN, 2015)

Taking machine learning research online with OpenML. - Joaquin Vanschoren; Jan N. van Rijn; Bernd Bischl (BigMine, 2015)

Algorithm Selection via Meta-learning and Sample-based Active Testing. - Salisu Mamman Abdulrahman; Pavel Brazdil; Jan N. van Rijn; Joaquin Vanschoren (MetaSel@PKDD/ECML, 2015)

Meta-learning Recommendation of Default Hyper-parameter Values for SVMs in Classification Tasks. - Rafael Gomes Mantovani; André L. D. Rossi; Joaquin Vanschoren; André C. P. L. F. de Carvalho (MetaSel@PKDD/ECML, 2015)

Sharing RapidMiner Workflows and Experiments with OpenML. - Jan N. van Rijn; Joaquin Vanschoren (MetaSel@PKDD/ECML, 2015)

Towards a Collaborative Platform for Advanced Meta-Learning in Healthcare Predictive Analytics. - Milan Vukicevic; Sandro Radovanovic; Joaquin Vanschoren; Giulio Napolitano; Boris Delibasic (MetaSel@PKDD/ECML, 2015)

Hyper-Parameter Tuning of a Decision Tree Induction Algorithm. - Rafael Gomes Mantovani; Tomás Horváth; Ricardo Cerri; Joaquin Vanschoren; André C. P. L. F. de Carvalho (BRACIS, 2016)

Anticipating habit formation: a psychological computing approach to behavior change support. - Chao Zhang 071; Arlette van Wissen; Daniël Lakens; Joaquin Vanschoren; Boris E. R. de Ruyter; Wijnand A. IJsselsteijn (UbiComp Adjunct, 2016)

An Algorithm, Implementation and Execution Ontology Design Pattern. - Agnieszka Lawrynowicz; Diego Esteves; Pance Panov; Tommaso Soru; Saso Dzeroski; Joaquin Vanschoren (WOP@ISWC, 2016)

ASlib: A benchmark library for algorithm selection. - Bernd Bischl; Pascal Kerschke; Lars Kotthoff; Marius Lindauer; Yuri Malitsky; Alexandre Fréchette; Holger H. Hoos; Frank Hutter; Kevin Leyton-Brown; Kevin Tierney; Joaquin Vanschoren (Artif. Intell., 2016)

Toward understanding online sentiment expression: an interdisciplinary approach with subgroup comparison and visualization. - Bo Gao 002; Bettina Berendt; Joaquin Vanschoren (Soc. Netw. Anal. Min., 2016)

Layered TPOT: Speeding up Tree-based Pipeline Optimization. - Pieter Gijsbers; Joaquin Vanschoren; Randal S. Olson (AutoML@PKDD/ECML, 2017)

OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML. - Giuseppe Casalicchio; Jakob Bossek; Michel Lang; Dominik Kirchhoff; Pascal Kerschke; Benjamin Hofner; Heidi Seibold; Joaquin Vanschoren; Bernd Bischl (CoRR, 2017)

OpenML Benchmarking Suites and the OpenML100. - Bernd Bischl; Giuseppe Casalicchio; Matthias Feurer; Frank Hutter; Michel Lang; Rafael Gomes Mantovani; Jan N. van Rijn; Joaquin Vanschoren (CoRR, 2017)

Data Augmentation using Conditional Generative Adversarial Networks for Leaf Counting in Arabidopsis Plants. - Yezi Zhu; Marc Aoun; Marcel Krijn; Joaquin Vanschoren (BMVC, 2018)

ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies. - Gustavo Correa Publio; Diego Esteves; Agnieszka Lawrynowicz; Pance Panov; Larisa N. Soldatova; Tommaso Soru; Joaquin Vanschoren; Hamid Zafar (CoRR, 2018)

Towards Reproducible Empirical Research in Meta-Learning. - Adriano Rivolli; Luís Paulo F. Garcia; Carlos Soares; Joaquin Vanschoren; André C. P. L. F. de Carvalho (CoRR, 2018)

Meta-Learning: A Survey. - Joaquin Vanschoren (CoRR, 2018)

Transformative Machine Learning. - Iván Olier; Oghenejokpeme I. Orhobor; Joaquin Vanschoren; Ross D. King (CoRR, 2018)

An empirical study on hyperparameter tuning of decision trees. - Rafael Gomes Mantovani; Tomás Horváth; Ricardo Cerri; Sylvio Barbon Junior; Joaquin Vanschoren; André Carlos Ponce de Leon Ferreira de Carvalho (CoRR, 2018)

Speeding up algorithm selection using average ranking and active testing by introducing runtime. - Salisu Mamman Abdulrahman; Pavel Brazdil; Jan N. van Rijn; Joaquin Vanschoren (Mach. Learn., 2018)

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery. - Iván Olier; Noureddin Sadawi; G. Richard J. Bickerton; Joaquin Vanschoren; Crina Grosan; Larisa N. Soldatova; Ross D. King (Mach. Learn., 2018)

The online performance estimation framework: heterogeneous ensemble learning for data streams. - Jan N. van Rijn; Geoffrey Holmes 001; Bernhard Pfahringer; Joaquin Vanschoren (Mach. Learn., 2018)

Meta-Learning. - Joaquin Vanschoren (Automated Machine Learning, 2019)

The ABC of Data: A Classifying Framework for Data Readiness. - Laurens A. Castelijns; Yuri Maas; Joaquin Vanschoren (PKDD/ECML Workshops (1), 2019)

Beyond Bag-of-Concepts: Vectors of Locally Aggregated Concepts. - Maarten Grootendorst; Joaquin Vanschoren (ECML/PKDD (2), 2019)

SysML: The New Frontier of Machine Learning Systems. - Alexander Ratner; Dan Alistarh; Gustavo Alonso; David G. Andersen; Peter Bailis; Sarah Bird; Nicholas Carlini; Bryan Catanzaro; Eric S. Chung; Bill Dally; Jeff Dean; Inderjit S. Dhillon; et al. (CoRR, 2019)

An Open Source AutoML Benchmark. - Pieter Gijsbers; Erin LeDell; Janek Thomas; Sébastien Poirier; Bernd Bischl; Joaquin Vanschoren (CoRR, 2019)

Learning to reinforcement learn for Neural Architecture Search. - J. Gomez Robles; Joaquin Vanschoren (CoRR, 2019)

OpenML: An R package to connect to the machine learning platform OpenML. - Giuseppe Casalicchio; Jakob Bossek; Michel Lang; Dominik Kirchhoff; Pascal Kerschke; Benjamin Hofner; Heidi Seibold; Joaquin Vanschoren; Bernd Bischl (Comput. Stat., 2019)

A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers. - Rafael Gomes Mantovani; André L. D. Rossi; Edesio Alcobaça; Joaquin Vanschoren; André C. P. L. F. de Carvalho (Inf. Sci., 2019)

Multi-task learning with a natural metric for quantitative structure activity relationship learning. - Noureddin Sadawi; Iván Olier; Joaquin Vanschoren; Jan N. van Rijn; Jeremy Besnard; G. Richard J. Bickerton; Crina Grosan; Larisa N. Soldatova; Ross D. King (J. Cheminformatics, 2019)

GAMA: Genetic Automated Machine learning Assistant. - Pieter Gijsbers; Joaquin Vanschoren (J. Open Source Softw., 2019)

GAMA: A General Automated Machine Learning Assistant. - Pieter Gijsbers; Joaquin Vanschoren (ECML/PKDD (5), 2020)

Importance of Tuning Hyperparameters of Machine Learning Algorithms. - Hilde J. P. Weerts; Andreas C. Müller 002; Joaquin Vanschoren (CoRR, 2020)

Aerial Imagery Pixel-level Segmentation. - Michael R. Heffels; Joaquin Vanschoren (CoRR, 2020)

Guest editors' introduction to the special issue on Discovery Science. - Larisa N. Soldatova; Joaquin Vanschoren (Mach. Learn., 2020)

Advances in MetaDL: AAAI 2021 Challenge and Workshop. - Adrian El Baz; Isabelle Guyon; Zhengying Liu; Jan N. van Rijn; Sébastien Treguer; Joaquin Vanschoren (MetaDL@AAAI, 2021)

Meta-learning for symbolic hyperparameter defaults. - Pieter Gijsbers; Florian Pfisterer; Jan N. van Rijn; Bernd Bischl; Joaquin Vanschoren (GECCO Companion, 2021)

OpenML Benchmarking Suites. - Bernd Bischl; Giuseppe Casalicchio; Matthias Feurer; Pieter Gijsbers; Frank Hutter; Michel Lang; Rafael Gomes Mantovani; Jan N. van Rijn; Joaquin Vanschoren (NeurIPS Datasets and Benchmarks, 2021)

NeurIPS’22 Cross-Domain MetaDL Challenge: Results and lessons learned. - Dustin Carrión-Ojeda; Mahbubul Alam; Sergio Escalera; Ahmed K. Farahat; Dipanjan Ghosh; Maria Teresa Gonzalez Diaz; Chetan Gupta 001; Isabelle Guyon; Joël Roman Ky; Xian Yeow Lee; Xin Liu; Felix Mohr; et al. (NeurIPS (Competition and Demos), 2021)

Theory-based Habit Modeling for Enhancing Behavior Prediction. - Chao Zhang 071; Joaquin Vanschoren; Arlette van Wissen; Daniël Lakens; Boris E. R. de Ruyter; Wijnand A. IJsselsteijn (CoRR, 2021)

Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models. - Jeroen van Hoof; Joaquin Vanschoren (CoRR, 2021)

Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms. - Rishabh Goyal; Joaquin Vanschoren; Victor van Acht; Stephan Nijssen (CoRR, 2021)

Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification. - Irma van den Brandt; Floris Fok; Bas Mulders; Joaquin Vanschoren; Veronika Cheplygina (CoRR, 2021)

From Strings to Data Science: a Practical Framework for Automated String Handling. - John W. van Lith; Joaquin Vanschoren (CoRR, 2021)

Frugal Machine Learning. - Mikhail Evchenko; Joaquin Vanschoren; Holger H. Hoos; Marc Schoenauer; Michèle Sebag (CoRR, 2021)

OpenML-Python: an extensible Python API for OpenML. - Matthias Feurer; Jan N. van Rijn; Arlind Kadra; Pieter Gijsbers; Neeratyoy Mallik; Sahithya Ravi; Andreas Müller 004; Joaquin Vanschoren; Frank Hutter (J. Mach. Learn. Res., 2021)

Adaptation Strategies for Automated Machine Learning on Evolving Data. - Bilge Celik; Joaquin Vanschoren (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Towards Scalable Online Machine Learning Collaborations with OpenML. - Joaquin Vanschoren (Proc. VLDB Endow., 2021)

Faster Performance Estimation for NAS with Embedding Proximity Score. - Gideon Franken; Prabhant Singh; Joaquin Vanschoren (Meta-Knowledge Transfer @ ECML/PKDD, 2022)

Multi-fidelity optimization method with asynchronous generalized island model for AutoML. - Israel Campero-Jurado; Joaquin Vanschoren (GECCO Companion, 2022)

Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification. - Ihsan Ullah; Dustin Carrión-Ojeda; Sergio Escalera; Isabelle Guyon; Mike Huisman; Felix Mohr; Jan N. van Rijn; Haozhe Sun; Joaquin Vanschoren; Phan Anh Vu (NeurIPS, 2022)

Automated Reinforcement Learning: An Overview. - Reza Refaei Afshar; Yingqian Zhang 001; Joaquin Vanschoren; Uzay Kaymak (CoRR, 2022)

Warm-starting DARTS using meta-learning. - Matej Grobelnik; Joaquin Vanschoren (CoRR, 2022)

EmProx: Neural Network Performance Estimation For Neural Architecture Search. - G. G. H. Franken; Prabhant Singh; Joaquin Vanschoren (CoRR, 2022)

Open-Ended Learning Strategies for Learning Complex Locomotion Skills. - Fangqin Zhou; Joaquin Vanschoren (CoRR, 2022)

DataPerf: Benchmarks for Data-Centric AI Development. - Mark Mazumder; Colby R. Banbury; Xiaozhe Yao; Bojan Karlas; William Gaviria Rojas; Sudnya Frederick Diamos; Greg Diamos; Lynn He; Douwe Kiela; David Jurado; David Kanter; Rafael Mosquera; et al. (CoRR, 2022)

Evaluating Continual Test-Time Adaptation for Contextual and Semantic Domain Shifts. - Tommie Kerssies; Joaquin Vanschoren; Mert Kiliçkaya (CoRR, 2022)

Meta-Learning for Unsupervised Outlier Detection with Optimal Transport. - Prabhant Singh; Joaquin Vanschoren (CoRR, 2022)

Automated Imbalanced Learning. - Prabhant Singh; Joaquin Vanschoren (CoRR, 2022)

Meta-features for meta-learning. - Adriano Rivolli; Luís Paulo F. Garcia; Carlos Soares; Joaquin Vanschoren; André C. P. L. F. de Carvalho (Knowl. Based Syst., 2022)

Interpretable Assessment of ST-Segment Deviation in ECG Time Series. - Israel Campero-Jurado; Andrejs Fedjajevs; Joaquin Vanschoren; Aarnout Brombacher (Sensors, 2022)

Introduction to the Special Section on AI in Manufacturing: Current Trends and Challenges. - Jefrey Lijffijt; Dimitra Gkorou; Pieter Van Hertum; Alexander Ypma; Mykola Pechenizkiy; Joaquin Vanschoren (SIGKDD Explor., 2022)

Theory-based habit modeling for enhancing behavior prediction in behavior change support systems. - Chao Zhang 071; Joaquin Vanschoren; Arlette van Wissen; Daniël Lakens; Boris E. R. de Ruyter; Wijnand A. IJsselsteijn (User Model. User Adapt. Interact., 2022)

Neural Architecture Search for Visual Anomaly Segmentation. - Tommie Kerssies; Joaquin Vanschoren (AutoML, 2023)

Locality-Aware Hyperspectral Classification. - Fangqin Zhou; Mert Kilickaya; Joaquin Vanschoren (BMVC, 2023)

Are Labels Needed for Incremental Instance Learning? - Mert Kilickaya; Joaquin Vanschoren (CVPR Workshops, 2023)

AutoML for Outlier Detection with Optimal Transport Distances. - Prabhant Singh; Joaquin Vanschoren (IJCAI, 2023)

Efficient-DASH: Automated Radar Neural Network Design Across Tasks and Datasets. - Thomas Boot; Nicolas Cazin; Willem P. Sanberg; Joaquin Vanschoren (IV, 2023)

DataPerf: Benchmarks for Data-Centric AI Development. - Mark Mazumder; Colby R. Banbury; Xiaozhe Yao; Bojan Karlas; William Gaviria Rojas; Sudnya Frederick Diamos; Greg Diamos; Lynn He; Alicia Parrish; Hannah Rose Kirk; Jessica Quaye; Charvi Rastogi; et al. (NeurIPS, 2023)

An Analysis of Evolutionary Migration Models for Multi-Objective, Multi-Fidelity Automl. - Israel Campero-Jurado; Joaquin Vanschoren (SMC, 2023)

Adaptive Regularization for Class-Incremental Learning. - Elif Ceren Gok Yildirim; Murat Onur Yildirim; Mert Kilickaya; Joaquin Vanschoren (CoRR, 2023)

What Can AutoML Do For Continual Learning? - Mert Kilickaya; Joaquin Vanschoren (CoRR, 2023)

DMLR: Data-centric Machine Learning Research - Past, Present and Future. - Luis Oala; Manil Maskey; Lilith Bat-Leah; Alicia Parrish; Nezihe Merve Gürel; Tzu-Sheng Kuo; Yang Liu 018; Rotem Dror; Danilo Brajovic; Xiaozhe Yao; Max Bartolo; William Gaviria Rojas; et al. (CoRR, 2023)

Signal Quality Analysis for Long-Term ECG Monitoring Using a Health Patch in Cardiac Patients. - Israel Campero-Jurado; Ilde Lorato; John F. Morales; Lonneke Fruytier; Shavini Stuart; Pradeep Panditha; Daan M. Janssen; Nicolò Rossetti; Natallia Uzunbajakava; Irina Bianca Serban; Lars Rikken; Margreet de Kok; et al. (Sensors, 2023)

Online AutoML: an adaptive AutoML framework for online learning. - Bilge Celik; Prabhant Singh; Joaquin Vanschoren (Mach. Learn., 2023)

Learning to learn without forgetting using attention. - Anna Vettoruzzo; Joaquin Vanschoren; Mohamed-Rafik Bouguelia; Thorsteinn S. Rögnvaldsson (CoLLAs, 2024)

Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates. - Murat Onur Yildirim; Elif Ceren Gok Yildirim; Ghada Sokar; Decebal Constantin Mocanu; Joaquin Vanschoren (CPAL, 2024)

Croissant: A Metadata Format for ML-Ready Datasets. - Mubashara Akhtar; Omar Benjelloun; Costanza Conforti; Pieter Gijsbers; Joan Giner-Miguelez; Nitisha Jain; Michael Kuchnik; Quentin Lhoest; Pierre Marcenac; Manil Maskey; Peter Mattson; Luis Oala; et al. (DEEM@SIGMOD, 2024)

HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis. - Fangqin Zhou; Mert Kilickaya; Joaquin Vanschoren; Ran Piao (ECCV (32), 2024)

Position: TrustLLM: Trustworthiness in Large Language Models. - Yue Huang; Lichao Sun 001; Haoran Wang 005; Siyuan Wu 001; Qihui Zhang; Yuan Li; Chujie Gao; Yixin Huang; Wenhan Lyu; Yixuan Zhang; Xiner Li; Hanchi Sun; et al. (ICML, 2024)

MALIBO: Meta-learning for Likelihood-free Bayesian Optimization. - Jiarong Pan; Stefan Falkner; Felix Berkenkamp; Joaquin Vanschoren (ICML, 2024)

Croissant: A Metadata Format for ML-Ready Datasets. - Mubashara Akhtar; Omar Benjelloun; Costanza Conforti; Luca Foschini 002; Joan Giner-Miguelez; Pieter Gijsbers; Sujata S. Goswami; Nitisha Jain; Michalis Karamousadakis; Michael Kuchnik; Satyapriya Krishna; Sylvain Lesage; et al. (NeurIPS, 2024)

Automatic Combination of Sample Selection Strategies for Few-Shot Learning. - Branislav Pecher; Ivan Srba; Mária Bieliková; Joaquin Vanschoren (CoRR, 2024)

FOCIL: Finetune-and-Freeze for Online Class Incremental Learning by Training Randomly Pruned Sparse Experts. - Murat Onur Yildirim; Elif Ceren Gok Yildirim; Decebal Constantin Mocanu; Joaquin Vanschoren (CoRR, 2024)

Croissant: A Metadata Format for ML-Ready Datasets. - Mubashara Akhtar; Omar Benjelloun; Costanza Conforti; Joan Giner-Miguelez; Nitisha Jain; Michael Kuchnik; Quentin Lhoest; Pierre Marcenac; Manil Maskey; Peter Mattson; Luis Oala; Pierre Ruyssen; et al. (CoRR, 2024)

Introducing v0.5 of the AI Safety Benchmark from MLCommons. - Bertie Vidgen; Adarsh Agrawal; Ahmed M. Ahmed 004; Victor Akinwande; Namir Al-Nuaimi; Najla Alfaraj; Elie Alhajjar; Lora Aroyo; Trupti Bavalatti; Borhane Blili-Hamelin; Kurt D. Bollacker; Rishi Bomassani; et al. (CoRR, 2024)

CLAMS: A System for Zero-Shot Model Selection for Clustering. - Prabhant Singh; Pieter Gijsbers; Murat Onur Yildirim; Elif Ceren Gok; Joaquin Vanschoren (CoRR, 2024)

Can time series forecasting be automated? A benchmark and analysis. - Anvitha Thirthapura Sreedhara; Joaquin Vanschoren (CoRR, 2024)

A Standardized Machine-readable Dataset Documentation Format for Responsible AI. - Nitisha Jain; Mubashara Akhtar; Joan Giner-Miguelez; Rajat Shinde; Joaquin Vanschoren; Steffen Vogler; Subhra S. Goswami; Yuhan Rao; Tim Santos; Luis Oala; Michalis Karamousadakis; Manil Maskey; et al. (CoRR, 2024)

Robust and Efficient Transfer Learning via Supernet Transfer in Warm-started Neural Architecture Search. - Prabhant Singh; Joaquin Vanschoren (CoRR, 2024)

Continual Learning on a Data Diet. - Elif Ceren Gok Yildirim; Murat Onur Yildirim; Joaquin Vanschoren (CoRR, 2024)

On dataset transferability in medical image classification. - Dovile Juodelyte; Enzo Ferrante; Yucheng Lu 002; Prabhant Singh; Joaquin Vanschoren; Veronika Cheplygina (CoRR, 2024)

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. - Hilde J. P. Weerts; Florian Pfisterer; Matthias Feurer; Katharina Eggensperger; Edward Bergman; Noor H. Awad; Joaquin Vanschoren; Mykola Pechenizkiy; Bernd Bischl; Frank Hutter (J. Artif. Intell. Res., 2024)

AMLB: an AutoML Benchmark. - Pieter Gijsbers; Marcos L. P. Bueno; Stefan Coors; Erin LeDell; Sébastien Poirier; Janek Thomas; Bernd Bischl; Joaquin Vanschoren (J. Mach. Learn. Res., 2024)

Advances and Challenges in Meta-Learning: A Technical Review. - Anna Vettoruzzo; Mohamed-Rafik Bouguelia; Joaquin Vanschoren; Thorsteinn S. Rögnvaldsson; KC Santosh (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms. - Rafael Gomes Mantovani; Tomás Horváth; André L. D. Rossi; Ricardo Cerri; Sylvio Barbon Junior; Joaquin Vanschoren; André C. P. L. F. de Carvalho (Data Min. Knowl. Discov., 2024)

Towards efficient AutoML: a pipeline synthesis approach leveraging pre-trained transformers for multimodal data. - Ambarish Moharil; Joaquin Vanschoren; Prabhant Singh; Damian A. Tamburri (Mach. Learn., 2024)

Unsupervised Meta-Learning via In-Context Learning. - Anna Vettoruzzo; Lorenzo Braccaioli; Joaquin Vanschoren; Marlena Nowaczyk (ICLR, 2025)

OpenML: Insights from 10 years and more than a thousand papers. - Bernd Bischl; Giuseppe Casalicchio; Taniya Das; Matthias Feurer; Sebastian Fischer; Pieter Gijsbers; Subhaditya Mukherjee; Andreas C. Müller 002; László Németh; Luis Oala; Lennart Purucker; Sahithya Ravi; et al. (Patterns, 2025)

Occam's model: Selecting simpler representations for better transferability estimation. - Prabhant Singh; Sibylle Hess; Joaquin Vanschoren (CoRR, 2025)

Sculpting [CLS] Features for Pre-Trained Model-Based Class-Incremental Learning. - Murat Onur Yildirim; Elif Ceren Gok Yildirim; Joaquin Vanschoren (CoRR, 2025)

AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons. - Shaona Ghosh; Heather Frase; Adina Williams; Sarah Luger; Paul Röttger; Fazl Barez; Sean McGregor; Kenneth Fricklas; Mala Kumar; Quentin Feuillade-Montixi; Kurt Bollacker; Felix Friedrich; et al. (CoRR, 2025)

AutoML Benchmark with shorter time constraints and early stopping. - Israel Campero-Jurado; Pieter Gijsbers; Joaquin Vanschoren (CoRR, 2025)

CrypticBio: A Large Multimodal Dataset for Visually Confusing Biodiversity. - Georgiana Manolache; Gerard Schouten; Joaquin Vanschoren (CoRR, 2025)

Evolving Machine Learning: A Survey. - Ignacio Cabrera Martin; Subhaditya Mukherjee; Almas Baimagambetov; Joaquin Vanschoren; Nikolaos Polatidis (CoRR, 2025)

Meta-Learning Transformers to Improve In-Context Generalization. - Lorenzo Braccaioli; Anna Vettoruzzo; Prabhant Singh; Joaquin Vanschoren; Mohamed-Rafik Bouguelia; Nicola Conci (CoRR, 2025)