Joaquin Vanschoren

Orcid: 0000-0001-7044-9805

Affiliations:
  • Eindhoven University of Technology, The Netherlands


According to our database1, Joaquin Vanschoren authored at least 123 papers between 2007 and 2024.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Towards efficient AutoML: a pipeline synthesis approach leveraging pre-trained transformers for multimodal data.
Mach. Learn., September, 2024

Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms.
Data Min. Knowl. Discov., May, 2024

Advances and Challenges in Meta-Learning: A Technical Review.
IEEE Trans. Pattern Anal. Mach. Intell., 2024

AMLB: an AutoML Benchmark.
J. Mach. Learn. Res., 2024

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML.
J. Artif. Intell. Res., 2024

Continual Learning on a Data Diet.
CoRR, 2024

Learning to Learn without Forgetting using Attention.
CoRR, 2024

Robust and Efficient Transfer Learning via Supernet Transfer in Warm-started Neural Architecture Search.
CoRR, 2024

A Standardized Machine-readable Dataset Documentation Format for Responsible AI.
CoRR, 2024

Can time series forecasting be automated? A benchmark and analysis.
CoRR, 2024

CLAMS: A System for Zero-Shot Model Selection for Clustering.
CoRR, 2024

Unsupervised Meta-Learning via In-Context Learning.
CoRR, 2024

Introducing v0.5 of the AI Safety Benchmark from MLCommons.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
CoRR, 2024

Croissant: A Metadata Format for ML-Ready Datasets.
CoRR, 2024

FOCIL: Finetune-and-Freeze for Online Class Incremental Learning by Training Randomly Pruned Sparse Experts.
CoRR, 2024

Automatic Combination of Sample Selection Strategies for Few-Shot Learning.
CoRR, 2024

MALIBO: Meta-learning for Likelihood-free Bayesian Optimization.
Proceedings of the Forty-first International Conference on Machine Learning, 2024


HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis.
Proceedings of the Computer Vision - ECCV 2024, 2024


2023
Online AutoML: an adaptive AutoML framework for online learning.
Mach. Learn., June, 2023

Signal Quality Analysis for Long-Term ECG Monitoring Using a Health Patch in Cardiac Patients.
Sensors, February, 2023

DMLR: Data-centric Machine Learning Research - Past, Present and Future.
CoRR, 2023

What Can AutoML Do For Continual Learning?
CoRR, 2023

Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates.
CoRR, 2023

Adaptive Regularization for Class-Incremental Learning.
CoRR, 2023

An Analysis of Evolutionary Migration Models for Multi-Objective, Multi-Fidelity Automl.
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2023


Efficient-DASH: Automated Radar Neural Network Design Across Tasks and Datasets.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2023

AutoML for Outlier Detection with Optimal Transport Distances.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Are Labels Needed for Incremental Instance Learning?
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Locality-Aware Hyperspectral Classification.
Proceedings of the 34th British Machine Vision Conference 2023, 2023

Neural Architecture Search for Visual Anomaly Segmentation.
Proceedings of the International Conference on Automated Machine Learning, 2023

2022
Theory-based habit modeling for enhancing behavior prediction in behavior change support systems.
User Model. User Adapt. Interact., 2022

Introduction to the Special Section on AI in Manufacturing: Current Trends and Challenges.
SIGKDD Explor., 2022

Interpretable Assessment of ST-Segment Deviation in ECG Time Series.
Sensors, 2022

Meta-features for meta-learning.
Knowl. Based Syst., 2022

Automated Imbalanced Learning.
CoRR, 2022

Meta-Learning for Unsupervised Outlier Detection with Optimal Transport.
CoRR, 2022

Evaluating Continual Test-Time Adaptation for Contextual and Semantic Domain Shifts.
CoRR, 2022

DataPerf: Benchmarks for Data-Centric AI Development.
CoRR, 2022

Open-Ended Learning Strategies for Learning Complex Locomotion Skills.
CoRR, 2022

EmProx: Neural Network Performance Estimation For Neural Architecture Search.
CoRR, 2022

Warm-starting DARTS using meta-learning.
CoRR, 2022

Automated Reinforcement Learning: An Overview.
CoRR, 2022

Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Multi-fidelity optimization method with asynchronous generalized island model for AutoML.
Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

Faster Performance Estimation for NAS with Embedding Proximity Score.
Proceedings of the ECML/PKDD Workshop on Meta-Knowledge Transfer, 2022

2021
Towards Scalable Online Machine Learning Collaborations with OpenML.
Proc. VLDB Endow., 2021

Adaptation Strategies for Automated Machine Learning on Evolving Data.
IEEE Trans. Pattern Anal. Mach. Intell., 2021

OpenML-Python: an extensible Python API for OpenML.
J. Mach. Learn. Res., 2021

Frugal Machine Learning.
CoRR, 2021

From Strings to Data Science: a Practical Framework for Automated String Handling.
CoRR, 2021

Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification.
CoRR, 2021

Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms.
CoRR, 2021

Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models.
CoRR, 2021

Theory-based Habit Modeling for Enhancing Behavior Prediction.
CoRR, 2021


OpenML Benchmarking Suites.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Meta-learning for symbolic hyperparameter defaults.
Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

Advances in MetaDL: AAAI 2021 Challenge and Workshop.
Proceedings of the AAAI Workshop on Meta-Learning and MetaDL Challenge, 2021

2020
Guest editors' introduction to the special issue on Discovery Science.
Mach. Learn., 2020

Aerial Imagery Pixel-level Segmentation.
CoRR, 2020

Importance of Tuning Hyperparameters of Machine Learning Algorithms.
CoRR, 2020

GAMA: A General Automated Machine Learning Assistant.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, 2020

2019
GAMA: Genetic Automated Machine learning Assistant.
J. Open Source Softw., 2019

Multi-task learning with a natural metric for quantitative structure activity relationship learning.
J. Cheminformatics, 2019

A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers.
Inf. Sci., 2019

OpenML: An R package to connect to the machine learning platform OpenML.
Comput. Stat., 2019

Learning to reinforcement learn for Neural Architecture Search.
CoRR, 2019

An Open Source AutoML Benchmark.
CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.
CoRR, 2019

Beyond Bag-of-Concepts: Vectors of Locally Aggregated Concepts.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

The ABC of Data: A Classifying Framework for Data Readiness.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Meta-Learning.
Proceedings of the Automated Machine Learning - Methods, Systems, Challenges, 2019

2018
The online performance estimation framework: heterogeneous ensemble learning for data streams.
Mach. Learn., 2018

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery.
Mach. Learn., 2018

Speeding up algorithm selection using average ranking and active testing by introducing runtime.
Mach. Learn., 2018

An empirical study on hyperparameter tuning of decision trees.
CoRR, 2018

Transformative Machine Learning.
CoRR, 2018

Meta-Learning: A Survey.
CoRR, 2018

Towards Reproducible Empirical Research in Meta-Learning.
CoRR, 2018

ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies.
CoRR, 2018

Data Augmentation using Conditional Generative Adversarial Networks for Leaf Counting in Arabidopsis Plants.
Proceedings of the British Machine Vision Conference 2018, 2018

2017
OpenML Benchmarking Suites and the OpenML100.
CoRR, 2017

OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML.
CoRR, 2017

Layered TPOT: Speeding up Tree-based Pipeline Optimization.
Proceedings of the International Workshop on Automatic Selection, 2017

2016
Toward understanding online sentiment expression: an interdisciplinary approach with subgroup comparison and visualization.
Soc. Netw. Anal. Min., 2016

ASlib: A benchmark library for algorithm selection.
Artif. Intell., 2016

An Algorithm, Implementation and Execution Ontology Design Pattern.
Proceedings of the Advances in Ontology Design and Patterns [revised and extended versions of the papers presented at the 7th edition of the Workshop on Ontology and Semantic Web Patterns, 2016

Anticipating habit formation: a psychological computing approach to behavior change support.
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2016 ACM International Symposium on Wearable Computers, 2016

Hyper-Parameter Tuning of a Decision Tree Induction Algorithm.
Proceedings of the 5th Brazilian Conference on Intelligent Systems, 2016

2015
Towards a Collaborative Platform for Advanced Meta-Learning in Healthcare Predictive Analytics.
Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2015 (ECMLPKDD 2015), 2015

Sharing RapidMiner Workflows and Experiments with OpenML.
Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2015 (ECMLPKDD 2015), 2015

Meta-learning Recommendation of Default Hyper-parameter Values for SVMs in Classification Tasks.
Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2015 (ECMLPKDD 2015), 2015

Algorithm Selection via Meta-learning and Sample-based Active Testing.
Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2015 (ECMLPKDD 2015), 2015

Taking machine learning research online with OpenML.
Proceedings of the 4th International Workshop on Big Data, 2015

Effectiveness of Random Search in SVM hyper-parameter tuning.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

To tune or not to tune: Recommending when to adjust SVM hyper-parameters via meta-learning.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Fast Algorithm Selection Using Learning Curves.
Proceedings of the Advances in Intelligent Data Analysis XIV, 2015

Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams.
Proceedings of the 2015 IEEE International Conference on Data Mining, 2015

Decreasing the False Alarm Rate of Arrhythmias in Intensive Care Using a Machine Learning Approach.
Proceedings of the Computing in Cardiology, 2015

Who is More Positive in Private? Analyzing Sentiment Differences across Privacy Levels and Demographic Factors in Facebook Chats and Posts.
Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2015

2014
Open science in machine learning.
CoRR, 2014

Towards Meta-learning over Data Streams.
Proceedings of the International Workshop on Meta-learning and Algorithm Selection co-located with 21st European Conference on Artificial Intelligence, 2014

Algorithm Selection on Data Streams.
Proceedings of the Discovery Science - 17th International Conference, 2014

2013
OpenML: networked science in machine learning.
SIGKDD Explor., 2013

A survey of intelligent assistants for data analysis.
ACM Comput. Surv., 2013

OpenML: A Collaborative Science Platform.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

2012
Experiment databases - A new way to share, organize and learn from experiments.
Mach. Learn., 2012

MDL-Based Analysis of Time Series at Multiple Time-Scales.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Scientific Workflow Management with ADAMS.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Selecting Classification Algorithms with Active Testing.
Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2012

2011
Meta-Learning Architectures: Collecting, Organizing and Exploiting Meta-Knowledge.
Proceedings of the Meta-Learning in Computational Intelligence, 2011

Traffic Events Modeling for Structural Health Monitoring.
Proceedings of the Advances in Intelligent Data Analysis X - 10th International Symposium, 2011

Datenanalyse und -visualisierung.
Proceedings of the Handbuch Forschungsdatenmanagement., 2011

2010
Understanding Machine Learning Performance with Experiment Databases (Het verwerven van inzichten in leerperformantie met experiment databanken) ; Understanding Machine Learning Performance with Experiment Databases.
PhD thesis, 2010

Experiment Databases.
Proceedings of the Inductive Databases and Constraint-Based Data Mining., 2010

2009
A Community-Based Platform for Machine Learning Experimentation.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009

2008
Learning from the Past with Experiment Databases.
Proceedings of the PRICAI 2008: Trends in Artificial Intelligence, 2008

Organizing the World's Machine Learning Information.
Proceedings of the Leveraging Applications of Formal Methods, 2008

2007
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning.
Proceedings of the Knowledge Discovery in Databases: PKDD 2007, 2007

Investigating Classifier Learning Behavior with Experiment Databases.
Proceedings of the Data Analysis, Machine Learning and Applications, 2007


  Loading...