Joaquin Vanschoren

176 papers A* 8A 2B 10C 2Journal 105Unranked 35
YearRankTypeTitle / Venue / Authors
2026 — conf
An Empirical Study of Speculative Decoding for Small Language Models.
EACL (Volume 1: Long Papers)
Luca Mainardi, Selçuk Sandikci, Joaquin Vanschoren
2026 J jnl
Automated Machine Learning for Unsupervised Tabular Tasks.
Mach. Learn.
Prabhant Singh, Pieter Gijsbers, Elif Ceren Gok Yildirim, Murat Onur Yildirim, Joaquin Vanschoren
2026 J jnl
Pruned Adaptation Modules: A Simple yet Strong Baseline for Continual Foundation Models.
CoRR
Elif Ceren Gok Yildirim, Murat Onur Yildirim, Joaquin Vanschoren
2026 J jnl
Robustness of AutoML on Dirty Categorical Data.
CoRR
Marcos L. P. Bueno, Joaquin Vanschoren
2026 J jnl
Unlocking [CLS] Features for Continual Post-Training.
Trans. Mach. Learn. Res.
Murat Onur Yildirim, Elif Ceren Gok Yildirim, Joaquin Vanschoren
2025 J jnl
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons.
CoRR
Shaona Ghosh, Heather Frase, Adina Williams, Sarah Luger, Paul Röttger, Fazl Barez, Sean McGregor, Kenneth Fricklas, Mala Kumar, Quentin Feuillade-Montixi, Kurt D. Bollacker, Felix Friedrich, Ryan Tsang, Bertie Vidgen, Alicia Parrish, Chris Knotz, Eleonora Presani, Jonathan Bennion, Marisa Ferrara Boston, Mike Kuniavsky, Wiebke Hutiri, James Ezick, Malek Ben Salem, Rajat Sahay, Sujata S. Goswami, Usman Gohar, Ben Huang, Supheakmungkol Sarin, Elie Alhajjar, Canyu Chen, Roman Eng, Kashyap Ramanandula Manjusha, Virendra Mehta, Eileen Long, Murali Emani, Natan Vidra, Benjamin Rukundo, Abolfazl Shahbazi, Kongtao Chen, Rajat Ghosh, Vithursan Thangarasa, Pierre Peigné, Abhinav Singh, Max Bartolo, Satyapriya Krishna, Mubashara Akhtar, Rafael Gold, Cody Coleman, Luis Oala, Vassil Tashev, Joseph Marvin Imperial, Amy Russ, Sasidhar Kunapuli, Nicolas Miailhe, Julien Delaunay, Bhaktipriya Radharapu, Rajat Shinde, Tuesday, Debojyoti Dutta, Declan Grabb, Ananya Gangavarapu, Saurav Sahay, Agasthya Gangavarapu, Patrick Schramowski, Stephen Singam, Tom David, Xudong Han, Priyanka Mary Mammen, Tarunima Prabhakar, Venelin Kovatchev, Ahmed Ahmed, Kelvin N. Manyeki, Sandeep Madireddy, Foutse Khomh, Fedor Zhdanov, Joachim Baumann, Nina Vasan, Xianjun Yang, Carlos Mougn, Jibin Rajan Varghese, Hussain Chinoy, Seshakrishna Jitendar, Manil Maskey, Claire V. Hardgrove, Tianhao Li, Aakash Gupta, Emil Joswin, Yifan Mai, Shachi H. Kumar, Cigdem Patlak, Kevin Lu, Vincent Alessi, Sree Bhargavi Balija, Chenhe Gu, Robert Sullivan, James Gealy, Matt Lavrisa, James Goel, Peter Mattson, Percy Liang, Joaquin Vanschoren
2025 J jnl
AutoML Benchmark with shorter time constraints and early stopping.
CoRR
Israel Campero-Jurado, Pieter Gijsbers, Joaquin Vanschoren
2025 J jnl
Automated Machine Learning for Unsupervised Tabular Tasks.
CoRR
Prabhant Singh, Pieter Gijsbers, Elif Ceren Gok Yildirim, Murat Onur Yildirim, Joaquin Vanschoren
2025 J jnl
CrypticBio: A Large Multimodal Dataset for Visually Confusing Biodiversity.
CoRR
Georgiana Manolache, Gerard Schouten, Joaquin Vanschoren
2025 J jnl
Evolving Machine Learning: A Survey.
CoRR
Ignacio Cabrera Martin, Subhaditya Mukherjee, Almas Baimagambetov, Joaquin Vanschoren, Nikolaos Polatidis
2025 J jnl
How NOT to benchmark your SITE metric: Beyond Static Leaderboards and Towards Realistic Evaluation.
CoRR
Prabhant Singh, Sibylle Hess, Joaquin Vanschoren
2025 J jnl
Meta-Learning Transformers to Improve In-Context Generalization.
CoRR
Lorenzo Braccaioli, Anna Vettoruzzo, Prabhant Singh, Joaquin Vanschoren, Mohamed-Rafik Bouguelia, Nicola Conci
2025 J jnl
Occam's model: Selecting simpler representations for better transferability estimation.
CoRR
Prabhant Singh, Sibylle Hess, Joaquin Vanschoren
2025 J jnl
OpenML: Insights from 10 years and more than a thousand papers.
Patterns
Bernd Bischl, Giuseppe Casalicchio, Taniya Das, Matthias Feurer, Sebastian Fischer, Pieter Gijsbers, Subhaditya Mukherjee, Andreas C. Müller, László Németh, Luis Oala, Lennart Purucker, Sahithya Ravi, Jan N. van Rijn, Prabhant Singh, Joaquin Vanschoren, Jos van der Velde, Marcel Wever
2025 B conf
Score Matching on Large Geometric Graphs for Cosmology Generation.
DS
Diana-Alexandra Onutu, Yue Zhao, Joaquin Vanschoren, Vlado Menkovski
2025 J jnl
Score Matching on Large Geometric Graphs for Cosmology Generation.
CoRR
Diana-Alexandra Onutu, Yue Zhao, Joaquin Vanschoren, Vlado Menkovski
2025 J jnl
Sculpting [CLS] Features for Pre-Trained Model-Based Class-Incremental Learning.
CoRR
Murat Onur Yildirim, Elif Ceren Gok Yildirim, Joaquin Vanschoren
2025 A* conf
Unsupervised Meta-Learning via In-Context Learning.
ICLR
Anna Vettoruzzo, Lorenzo Braccaioli, Joaquin Vanschoren, Marlena Nowaczyk
2024 J jnl
A Standardized Machine-readable Dataset Documentation Format for Responsible AI.
CoRR
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, Pierre Marcenac, Costanza Conforti, Michael Kuchnik, Lora Aroyo, Omar Benjelloun, Elena Simperl
2024 J jnl
AMLB: an AutoML Benchmark.
J. Mach. Learn. Res.
Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren
2024 J jnl
Advances and Challenges in Meta-Learning: A Technical Review.
IEEE Trans. Pattern Anal. Mach. Intell.
Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Joaquin Vanschoren, Thorsteinn S. Rögnvaldsson, KC Santosh
2024 J jnl
Automatic Combination of Sample Selection Strategies for Few-Shot Learning.
CoRR
Branislav Pecher, Ivan Srba, Mária Bieliková, Joaquin Vanschoren
2024 J jnl
Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms.
Data Min. Knowl. Discov.
Rafael Gomes Mantovani, Tomás Horváth, André L. D. Rossi, Ricardo Cerri, Sylvio Barbon Junior, Joaquin Vanschoren, André C. P. L. F. de Carvalho
2024 J jnl
CLAMS: A System for Zero-Shot Model Selection for Clustering.
CoRR
Prabhant Singh, Pieter Gijsbers, Murat Onur Yildirim, Elif Ceren Gok, Joaquin Vanschoren
2024 J jnl
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML.
J. Artif. Intell. Res.
Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter
2024 J jnl
Can time series forecasting be automated? A benchmark and analysis.
CoRR
Anvitha Thirthapura Sreedhara, Joaquin Vanschoren
2024 J jnl
Continual Learning on a Data Diet.
CoRR
Elif Ceren Gok Yildirim, Murat Onur Yildirim, Joaquin Vanschoren
2024 — conf
Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates.
CPAL
Murat Onur Yildirim, Elif Ceren Gok Yildirim, Ghada Sokar, Decebal Constantin Mocanu, Joaquin Vanschoren
2024 — conf
Croissant: A Metadata Format for ML-Ready Datasets.
DEEM@SIGMOD
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, Pierre Ruyssen, Rajat Shinde, Elena Simperl, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Jos van der Velde, Steffen Vogler, Carole-Jean Wu
2024 A* conf
Croissant: A Metadata Format for ML-Ready Datasets.
NeurIPS
Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Luca Foschini, Joan Giner-Miguelez, Pieter Gijsbers, Sujata S. Goswami, Nitisha Jain, Michalis Karamousadakis, Michael Kuchnik, Satyapriya Krishna, Sylvain Lesage, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Hamidah Oderinwale, Pierre Ruyssen, Tim Santos, Rajat Shinde, Elena Simperl, Arjun Suresh, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Susheel Varma, Jos van der Velde, Steffen Vogler, Carole-Jean Wu, Luyao Zhang
2024 J jnl
Croissant: A Metadata Format for ML-Ready Datasets.
CoRR
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, Rajat Shinde, Elena Simperl, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Steffen Vogler, Carole-Jean Wu
2024 J jnl
DMLR: Data-centric Machine Learning Research - Past, Present and Future.
J. Data-centric Mach. Learn. Res.
Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, Max Bartolo, William Gaviria Rojas, Ryan Hileman, Rainier Aliment, Michael W. Mahoney, Meg Risdal, Matthew Lease, Wojciech Samek, Debojyoti Dutta, Curtis G. Northcutt, Cody Coleman, Braden Hancock, Bernard Koch, Girmaw Abebe Tadesse, Bojan Karlas, Ahmed Alaa, Adji Bousso Dieng, Natasha F. Noy, Vijay Janapa Reddi, James Zou, Praveen K. Paritosh, Mihaela van der Schaar, Kurt Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson
2024 J jnl
FOCIL: Finetune-and-Freeze for Online Class Incremental Learning by Training Randomly Pruned Sparse Experts.
CoRR
Murat Onur Yildirim, Elif Ceren Gok Yildirim, Decebal Constantin Mocanu, Joaquin Vanschoren
2024 — conf
HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis.
ECCV (32)
Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren, Ran Piao
2024 J jnl
HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis.
CoRR
Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren, Ran Piao
2024 — ed.
International Conference on Automated Machine Learning, 9-12 September 2024, Sorbonne Université, Paris, France.
AutoML
Katharina Eggensperger, Roman Garnett, Joaquin Vanschoren, Marius Lindauer, Jacob R. Gardner
2024 J jnl
Introducing v0.5 of the AI Safety Benchmark from MLCommons.
CoRR
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Borhane Blili-Hamelin, Kurt Bollacker, Rishi Bomassani, Marisa Ferrara Boston, Siméon Campos, Kal Chakra, Canyu Chen, Cody Coleman, Zacharie Delpierre Coudert, Leon Derczynski, Debojyoti Dutta, Ian Eisenberg, James Ezick, Heather Frase, Brian Fuller, Ram Gandikota, Agasthya Gangavarapu, Ananya Gangavarapu, James Gealy, Rajat Ghosh, James Goel, Usman Gohar, Subhra S. Goswami, Scott A. Hale, Wiebke Hutiri, Joseph Marvin Imperial, Surgan Jandial, Nick Judd, Felix Juefei-Xu, Foutse Khomh, Bhavya Kailkhura, Hannah Rose Kirk, Kevin Klyman, Chris Knotz, Michael Kuchnik, Shachi H. Kumar, Chris Lengerich, Bo Li, Zeyi Liao, Eileen Peters Long, Victor Lu, Yifan Mai, Priyanka Mary Mammen, Kelvin N. Manyeki, Sean McGregor, Virendra Mehta, Shafee Mohammed, Emanuel Moss, Lama Nachman, Dinesh Jinenhally Naganna, Amin Nikanjam, Besmira Nushi, Luis Oala, Iftach Orr, Alicia Parrish, Cigdem Patlak, William Pietri, Forough Poursabzi-Sangdeh, Eleonora Presani, Fabrizio Puletti, Paul Röttger, Saurav Sahay, Tim Santos, Nino Scherrer, Alice Schoenauer Sebag, Patrick Schramowski, Abolfazl Shahbazi, Vin Sharma, Xudong Shen, Vamsi Sistla, Leonard Tang, Davide Testuggine, Vithursan Thangarasa, Elizabeth Anne Watkins, Rebecca Weiss, Chris Welty, Tyler Wilbers, Adina Williams, Carole-Jean Wu, Poonam Yadav, Xianjun Yang, Yi Zeng, Wenhui Zhang, Fedor Zhdanov, Jiacheng Zhu, Percy Liang, Peter Mattson, Joaquin Vanschoren
2024 J jnl
Learning to Learn without Forgetting using Attention.
CoRR
Anna Vettoruzzo, Joaquin Vanschoren, Mohamed-Rafik Bouguelia, Thorsteinn S. Rögnvaldsson
2024 — conf
Learning to learn without forgetting using attention.
CoLLAs
Anna Vettoruzzo, Joaquin Vanschoren, Mohamed-Rafik Bouguelia, Thorsteinn S. Rögnvaldsson
2024 A* conf
MALIBO: Meta-learning for Likelihood-free Bayesian Optimization.
ICML
Jiarong Pan, Stefan Falkner, Felix Berkenkamp, Joaquin Vanschoren
2024 J jnl
On dataset transferability in medical image classification.
CoRR
Dovile Juodelyte, Enzo Ferrante, Yucheng Lu, Prabhant Singh, Joaquin Vanschoren, Veronika Cheplygina
2024 A* conf
Position: TrustLLM: Trustworthiness in Large Language Models.
ICML
Yue Huang, Lichao Sun, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Hanchi Sun, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric P. Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, Joaquin Vanschoren, John C. Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, Yue Zhao
2024 J jnl
Robust and Efficient Transfer Learning via Supernet Transfer in Warm-started Neural Architecture Search.
CoRR
Prabhant Singh, Joaquin Vanschoren
2024 J jnl
Towards efficient AutoML: a pipeline synthesis approach leveraging pre-trained transformers for multimodal data.
Mach. Learn.
Ambarish Moharil, Joaquin Vanschoren, Prabhant Singh, Damian A. Tamburri
2024 J jnl
Unsupervised Meta-Learning via In-Context Learning.
CoRR
Anna Vettoruzzo, Lorenzo Braccaioli, Joaquin Vanschoren, Marlena Nowaczyk
2023 — conf
AdaCL: Adaptive Continual Learning.
CLAI Unconf
Elif Ceren Gok Yildirim, Murat Onur Yildirim, Mert Kilickaya, Joaquin Vanschoren
2023 J jnl
Adaptive Regularization for Class-Incremental Learning.
CoRR
Elif Ceren Gok Yildirim, Murat Onur Yildirim, Mert Kilickaya, Joaquin Vanschoren
2023 J jnl
Advances and Challenges in Meta-Learning: A Technical Review.
CoRR
Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Joaquin Vanschoren, Thorsteinn S. Rögnvaldsson, KC Santosh
2023 B conf
An Analysis of Evolutionary Migration Models for Multi-Objective, Multi-Fidelity Automl.
SMC
Israel Campero-Jurado, Joaquin Vanschoren
2023 — conf
Are Labels Needed for Incremental Instance Learning?
CVPR Workshops
Mert Kilickaya, Joaquin Vanschoren
2023 J jnl
Are Labels Needed for Incremental Instance Learning?
CoRR
Mert Kilickaya, Joaquin Vanschoren
2023 A* conf
AutoML for Outlier Detection with Optimal Transport Distances.
IJCAI
Prabhant Singh, Joaquin Vanschoren
2023 J jnl
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML.
CoRR
Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter
2023 J jnl
Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates.
CoRR
Murat Onur Yildirim, Elif Ceren Gok Yildirim, Ghada Sokar, Decebal Constantin Mocanu, Joaquin Vanschoren
2023 J jnl
DMLR: Data-centric Machine Learning Research - Past, Present and Future.
CoRR
Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, Max Bartolo, William Gaviria Rojas, Ryan Hileman, Rainier Aliment, Michael W. Mahoney, Meg Risdal, Matthew Lease, Wojciech Samek, Debojyoti Dutta, Curtis G. Northcutt, Cody Coleman, Braden Hancock, Bernard Koch, Girmaw Abebe Tadesse, Bojan Karlas, Ahmed M. Alaa, Adji Bousso Dieng, Natasha F. Noy, Vijay Janapa Reddi, James Zou, Praveen K. Paritosh, Mihaela van der Schaar, Kurt Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson
2023 A* conf
DataPerf: Benchmarks for Data-Centric AI Development.
NeurIPS
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, Douwe Kiela, David Jurado, David Kanter, Rafael Mosquera, Will Cukierski, Juan Ciro, Lora Aroyo, Bilge Acun, Lingjiao Chen, Mehul Raje, Max Bartolo, Evan Sabri Eyuboglu, Amirata Ghorbani, Emmett D. Goodman, Addison Howard, Oana Inel, Tariq Kane, Christine R. Kirkpatrick, D. Sculley, Tzu-Sheng Kuo, Jonas W. Mueller, Tristan Thrush, Joaquin Vanschoren, Margaret Warren, Adina Williams, Serena Yeung, Newsha Ardalani, Praveen K. Paritosh, Ce Zhang, James Y. Zou, Carole-Jean Wu, Cody Coleman, Andrew Y. Ng, Peter Mattson, Vijay Janapa Reddi
2023 C conf
Efficient-DASH: Automated Radar Neural Network Design Across Tasks and Datasets.
IV
Thomas Boot, Nicolas Cazin, Willem P. Sanberg, Joaquin Vanschoren
2023 A conf
Locality-Aware Hyperspectral Classification.
BMVC
Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren
2023 J jnl
Locality-Aware Hyperspectral Classification.
CoRR
Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren
2023 J jnl
MALIBO: Meta-learning for Likelihood-free Bayesian Optimization.
CoRR
Jiarong Pan, Stefan Falkner, Felix Berkenkamp, Joaquin Vanschoren
2023 J jnl
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification.
CoRR
Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N. van Rijn, Haozhe Sun, Joaquin Vanschoren, Phan Anh Vu
2023 — conf
Neural Architecture Search for Visual Anomaly Segmentation.
AutoML
Tommie Kerssies, Joaquin Vanschoren
2023 J jnl
Online AutoML: an adaptive AutoML framework for online learning.
Mach. Learn.
Bilge Celik, Prabhant Singh, Joaquin Vanschoren
2023 J jnl
Signal Quality Analysis for Long-Term ECG Monitoring Using a Health Patch in Cardiac Patients.
Sensors
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, Joaquin Vanschoren, Aarnout Brombacher
2023 J jnl
What Can AutoML Do For Continual Learning?
CoRR
Mert Kilickaya, Joaquin Vanschoren
2022 J jnl
AMLB: an AutoML Benchmark.
CoRR
Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren
2022 J jnl
Advances in MetaDL: AAAI 2021 challenge and workshop.
CoRR
Adrian El Baz, Isabelle Guyon, Zhengying Liu, Jan N. van Rijn, Sébastien Treguer, Joaquin Vanschoren
2022 J jnl
Automated Imbalanced Learning.
CoRR
Prabhant Singh, Joaquin Vanschoren
2022 J jnl
Automated Reinforcement Learning: An Overview.
CoRR
Reza Refaei Afshar, Yingqian Zhang, Joaquin Vanschoren, Uzay Kaymak
2022 J jnl
DataPerf: Benchmarks for Data-Centric AI Development.
CoRR
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, Juan Ciro, Lora Aroyo, Bilge Acun, Sabri Eyuboglu, Amirata Ghorbani, Emmett D. Goodman, Tariq Kane, Christine R. Kirkpatrick, Tzu-Sheng Kuo, Jonas Mueller, Tristan Thrush, Joaquin Vanschoren, Margaret Warren, Adina Williams, Serena Yeung, Newsha Ardalani, Praveen K. Paritosh, Ce Zhang, James Zou, Carole-Jean Wu, Cody Coleman, Andrew Y. Ng, Peter Mattson, Vijay Janapa Reddi
2022 J jnl
EmProx: Neural Network Performance Estimation For Neural Architecture Search.
CoRR
G. G. H. Franken, Prabhant Singh, Joaquin Vanschoren
2022 J jnl
Evaluating Continual Test-Time Adaptation for Contextual and Semantic Domain Shifts.
CoRR
Tommie Kerssies, Joaquin Vanschoren, Mert Kiliçkaya
2022 — conf
Faster Performance Estimation for NAS with Embedding Proximity Score.
Meta-Knowledge Transfer @ ECML/PKDD
Gideon Franken, Prabhant Singh, Joaquin Vanschoren
2022 J jnl
Interpretable Assessment of ST-Segment Deviation in ECG Time Series.
Sensors
Israel Campero-Jurado, Andrejs Fedjajevs, Joaquin Vanschoren, Aarnout Brombacher
2022 J jnl
Introduction to the Special Section on AI in Manufacturing: Current Trends and Challenges.
SIGKDD Explor.
Jefrey Lijffijt, Dimitra Gkorou, Pieter Van Hertum, Alexander Ypma, Mykola Pechenizkiy, Joaquin Vanschoren
2022 A* conf
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification.
NeurIPS
Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N. van Rijn, Haozhe Sun, Joaquin Vanschoren, Phan Anh Vu
2022 J jnl
Meta-Learning for Unsupervised Outlier Detection with Optimal Transport.
CoRR
Prabhant Singh, Joaquin Vanschoren
2022 J jnl
Meta-features for meta-learning.
Knowl. Based Syst.
Adriano Rivolli, Luís Paulo F. Garcia, Carlos Soares, Joaquin Vanschoren, André C. P. L. F. de Carvalho
2022 — conf
Multi-fidelity optimization method with asynchronous generalized island model for AutoML.
GECCO Companion
Israel Campero-Jurado, Joaquin Vanschoren
2022 J jnl
Online AutoML: An adaptive AutoML framework for online learning.
CoRR
Bilge Celik, Prabhant Singh, Joaquin Vanschoren
2022 J jnl
Open-Ended Learning Strategies for Learning Complex Locomotion Skills.
CoRR
Fangqin Zhou, Joaquin Vanschoren
2022 J jnl
Theory-based habit modeling for enhancing behavior prediction in behavior change support systems.
User Model. User Adapt. Interact.
Chao Zhang, Joaquin Vanschoren, Arlette van Wissen, Daniël Lakens, Boris E. R. de Ruyter, Wijnand A. IJsselsteijn
2022 J jnl
Warm-starting DARTS using meta-learning.
CoRR
Matej Grobelnik, Joaquin Vanschoren
2021 — ed.
AAAI Workshop on Meta-Learning and MetaDL Challenge, MetaDL@AAAI 2021, virtual, February 9, 2021.
MetaDL@AAAI
Isabelle Guyon, Jan N. van Rijn, Sébastien Treguer, Joaquin Vanschoren
2021 J jnl
Adaptation Strategies for Automated Machine Learning on Evolving Data.
IEEE Trans. Pattern Anal. Mach. Intell.
Bilge Celik, Joaquin Vanschoren
2021 — conf
Advances in MetaDL: AAAI 2021 Challenge and Workshop.
MetaDL@AAAI
Adrian El Baz, Isabelle Guyon, Zhengying Liu, Jan N. van Rijn, Sébastien Treguer, Joaquin Vanschoren
2021 J jnl
Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification.
CoRR
Irma van den Brandt, Floris Fok, Bas Mulders, Joaquin Vanschoren, Veronika Cheplygina
2021 J jnl
Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms.
CoRR
Rishabh Goyal, Joaquin Vanschoren, Victor van Acht, Stephan Nijssen
2021 J jnl
From Strings to Data Science: a Practical Framework for Automated String Handling.
CoRR
John W. van Lith, Joaquin Vanschoren
2021 J jnl
Frugal Machine Learning.
CoRR
Mikhail Evchenko, Joaquin Vanschoren, Holger H. Hoos, Marc Schoenauer, Michèle Sebag
2021 J jnl
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models.
CoRR
Jeroen van Hoof, Joaquin Vanschoren
2021 J jnl
Meta-Learning for Symbolic Hyperparameter Defaults.
CoRR
Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren
2021 — conf
Meta-learning for symbolic hyperparameter defaults.
GECCO Companion
Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren
2021 — conf
NeurIPS’22 Cross-Domain MetaDL Challenge: Results and lessons learned.
NeurIPS (Competition and Demos)
Dustin Carrión-Ojeda, Mahbubul Alam, Sergio Escalera, Ahmed K. Farahat, Dipanjan Ghosh, Maria Teresa Gonzalez Diaz, Chetan Gupta, Isabelle Guyon, Joël Roman Ky, Xian Yeow Lee, Xin Liu, Felix Mohr, Manh Hung Nguyen, Emmanuel Pintelas, Stefan Roth, Simone Schaub-Meyer, Haozhe Sun, Ihsan Ullah, Joaquin Vanschoren, Lasitha Vidyaratne, Jiamin Wu, Xiaotian Yin
2021 — conf
OpenML Benchmarking Suites.
NeurIPS Datasets and Benchmarks
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren
2021 J jnl
OpenML-Python: an extensible Python API for OpenML.
J. Mach. Learn. Res.
Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter
2021 — ed.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December 2021, virtual.
NeurIPS Datasets and Benchmarks
Joaquin Vanschoren, Sai-Kit Yeung
2021 J jnl
Theory-based Habit Modeling for Enhancing Behavior Prediction.
CoRR
Chao Zhang, Joaquin Vanschoren, Arlette van Wissen, Daniël Lakens, Boris E. R. de Ruyter, Wijnand A. IJsselsteijn
2021 J jnl
Towards Scalable Online Machine Learning Collaborations with OpenML.
Proc. VLDB Endow.
Joaquin Vanschoren
2020 J jnl
Adaptation Strategies for Automated Machine Learning on Evolving Data.
CoRR
Bilge Celik, Joaquin Vanschoren
2020 J jnl
Aerial Imagery Pixel-level Segmentation.
CoRR
Michael R. Heffels, Joaquin Vanschoren
2020 — conf
GAMA: A General Automated Machine Learning Assistant.
ECML/PKDD (5)
Pieter Gijsbers, Joaquin Vanschoren
2020 J jnl
GAMA: a General Automated Machine learning Assistant.
CoRR
Pieter Gijsbers, Joaquin Vanschoren
2020 J jnl
Guest editors' introduction to the special issue on Discovery Science.
Mach. Learn.
Larisa N. Soldatova, Joaquin Vanschoren
2020 J jnl
Importance of Tuning Hyperparameters of Machine Learning Algorithms.
CoRR
Hilde J. P. Weerts, Andreas C. Müller, Joaquin Vanschoren
2019 J jnl
A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers.
Inf. Sci.
Rafael Gomes Mantovani, André L. D. Rossi, Edesio Alcobaça, Joaquin Vanschoren, André C. P. L. F. de Carvalho
2019 J jnl
A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers.
CoRR
Rafael Gomes Mantovani, André Luis Debiaso Rossi, Edesio Alcobaça, Joaquin Vanschoren, André Carlos Ponce de Leon Ferreira de Carvalho
2019 J jnl
An Open Source AutoML Benchmark.
CoRR
Pieter Gijsbers, Erin LeDell, Janek Thomas, Sébastien Poirier, Bernd Bischl, Joaquin Vanschoren
2019 — book
Automated Machine Learning - Methods, Systems, Challenges
Automated Machine Learning
Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
2019 — conf
Beyond Bag-of-Concepts: Vectors of Locally Aggregated Concepts.
ECML/PKDD (2)
Maarten Grootendorst, Joaquin Vanschoren
2019 J jnl
GAMA: Genetic Automated Machine learning Assistant.
J. Open Source Softw.
Pieter Gijsbers, Joaquin Vanschoren
2019 J jnl
Learning to reinforcement learn for Neural Architecture Search.
CoRR
J. Gomez Robles, Joaquin Vanschoren
2019 — ch.
Meta-Learning.
Automated Machine Learning
Joaquin Vanschoren
2019 J jnl
Multi-task learning with a natural metric for quantitative structure activity relationship learning.
J. Cheminformatics
Noureddin Sadawi, Iván Olier, Joaquin Vanschoren, Jan N. van Rijn, Jeremy Besnard, G. Richard J. Bickerton, Crina Grosan, Larisa N. Soldatova, Ross D. King
2019 J jnl
OpenML-Python: an extensible Python API for OpenML.
CoRR
Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter
2019 J jnl
OpenML: An R package to connect to the machine learning platform OpenML.
Comput. Stat.
Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl
2019 J jnl
SysML: The New Frontier of Machine Learning Systems.
CoRR
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, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
2019 — conf
The ABC of Data: A Classifying Framework for Data Readiness.
PKDD/ECML Workshops (1)
Laurens A. Castelijns, Yuri Maas, Joaquin Vanschoren
2018 J jnl
An empirical study on hyperparameter tuning of decision trees.
CoRR
Rafael Gomes Mantovani, Tomás Horváth, Ricardo Cerri, Sylvio Barbon Junior, Joaquin Vanschoren, André Carlos Ponce de Leon Ferreira de Carvalho
2018 A conf
Data Augmentation using Conditional Generative Adversarial Networks for Leaf Counting in Arabidopsis Plants.
BMVC
Yezi Zhu, Marc Aoun, Marcel Krijn, Joaquin Vanschoren
2018 B ed.
Discovery Science - 21st International Conference, DS 2018, Limassol, Cyprus, October 29-31, 2018, Proceedings
DS
Larisa N. Soldatova, Joaquin Vanschoren, George A. Papadopoulos, Michelangelo Ceci
2018 J jnl
Layered TPOT: Speeding up Tree-based Pipeline Optimization.
CoRR
Pieter Gijsbers, Joaquin Vanschoren, Randal S. Olson
2018 J jnl
ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies.
CoRR
Gustavo Correa Publio, Diego Esteves, Agnieszka Lawrynowicz, Pance Panov, Larisa N. Soldatova, Tommaso Soru, Joaquin Vanschoren, Hamid Zafar
2018 J jnl
Meta-Learning: A Survey.
CoRR
Joaquin Vanschoren
2018 J jnl
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery.
Mach. Learn.
Iván Olier, Noureddin Sadawi, G. Richard J. Bickerton, Joaquin Vanschoren, Crina Grosan, Larisa N. Soldatova, Ross D. King
2018 J jnl
Speeding up algorithm selection using average ranking and active testing by introducing runtime.
Mach. Learn.
Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N. van Rijn, Joaquin Vanschoren
2018 J jnl
The online performance estimation framework: heterogeneous ensemble learning for data streams.
Mach. Learn.
Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren
2018 J jnl
Towards Reproducible Empirical Research in Meta-Learning.
CoRR
Adriano Rivolli, Luís Paulo F. Garcia, Carlos Soares, Joaquin Vanschoren, André C. P. L. F. de Carvalho
2018 J jnl
Transformative Machine Learning.
CoRR
Iván Olier, Oghenejokpeme I. Orhobor, Joaquin Vanschoren, Ross D. King
2017 — conf
Layered TPOT: Speeding up Tree-based Pipeline Optimization.
AutoML@PKDD/ECML
Pieter Gijsbers, Joaquin Vanschoren, Randal S. Olson
2017 J jnl
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery.
CoRR
Iván Olier, Noureddin Sadawi, G. Richard J. Bickerton, Joaquin Vanschoren, Crina Grosan, Larisa N. Soldatova, Ross D. King
2017 J jnl
OpenML Benchmarking Suites and the OpenML100.
CoRR
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren
2017 J jnl
OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML.
CoRR
Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl
2017 — ed.
Proceedings of the International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms co-located with the European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases, AutoML@PKDD/ECML 2017, Skopje, Macedonia, September 22, 2017.
AutoML@PKDD/ECML
Pavel Brazdil, Joaquin Vanschoren, Frank Hutter, Holger H. Hoos
2016 J jnl
ASlib: A benchmark library for algorithm selection.
Artif. Intell.
Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren
2016 — conf
An Algorithm, Implementation and Execution Ontology Design Pattern.
WOP@ISWC
Agnieszka Lawrynowicz, Diego Esteves, Pance Panov, Tommaso Soru, Saso Dzeroski, Joaquin Vanschoren
2016 — conf
Anticipating habit formation: a psychological computing approach to behavior change support.
UbiComp Adjunct
Chao Zhang, Arlette van Wissen, Daniël Lakens, Joaquin Vanschoren, Boris E. R. de Ruyter, Wijnand A. IJsselsteijn
2016 — conf
Hyper-Parameter Tuning of a Decision Tree Induction Algorithm.
BRACIS
Rafael Gomes Mantovani, Tomás Horváth, Ricardo Cerri, Joaquin Vanschoren, André C. P. L. F. de Carvalho
2016 — ed.
Learning and Intelligent Optimization - 10th International Conference, LION 10, Ischia, Italy, May 29 - June 1, 2016, Revised Selected Papers
LION
Paola Festa, Meinolf Sellmann, Joaquin Vanschoren
2016 — ed.
Proceedings of the 2016 Workshop on Automatic Machine Learning, AutoML 2016, co-located with 33rd International Conference on Machine Learning (ICML 2016), New York City, NY, USA, June 24, 2016
AutoML@ICML
Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
2016 J jnl
Toward understanding online sentiment expression: an interdisciplinary approach with subgroup comparison and visualization.
Soc. Netw. Anal. Min.
Bo Gao, Bettina Berendt, Joaquin Vanschoren
2015 J jnl
ASlib: A Benchmark Library for Algorithm Selection.
CoRR
Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren
2015 — conf
Algorithm Selection via Meta-learning and Sample-based Active Testing.
MetaSel@PKDD/ECML
Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N. van Rijn, Joaquin Vanschoren
2015 — conf
Decreasing the False Alarm Rate of Arrhythmias in Intensive Care Using a Machine Learning Approach.
CinC
Linda M. Eerikäinen, Joaquin Vanschoren, Michael Johannes Rooijakkers, Rik Vullings, Ronald M. Aarts
2015 B conf
Effectiveness of Random Search in SVM hyper-parameter tuning.
IJCNN
Rafael Gomes Mantovani, André Luis Debiaso Rossi, Joaquin Vanschoren, Bernd Bischl, André C. P. L. F. de Carvalho
2015 B conf
Fast Algorithm Selection Using Learning Curves.
IDA
Jan N. van Rijn, Salisu Mamman Abdulrahman, Pavel Brazdil, Joaquin Vanschoren
2015 A* conf
Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams.
ICDM
Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren
2015 — conf
Meta-learning Recommendation of Default Hyper-parameter Values for SVMs in Classification Tasks.
MetaSel@PKDD/ECML
Rafael Gomes Mantovani, André L. D. Rossi, Joaquin Vanschoren, André C. P. L. F. de Carvalho
2015 — ed.
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), Porto, Portugal, September 7th, 2015.
MetaSel@PKDD/ECML
Joaquin Vanschoren, Pavel Brazdil, Christophe G. Giraud-Carrier, Lars Kotthoff
2015 — conf
Sharing RapidMiner Workflows and Experiments with OpenML.
MetaSel@PKDD/ECML
Jan N. van Rijn, Joaquin Vanschoren
2015 — conf
Taking machine learning research online with OpenML.
BigMine
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl
2015 B conf
To tune or not to tune: Recommending when to adjust SVM hyper-parameters via meta-learning.
IJCNN
Rafael Gomes Mantovani, André Luis Debiaso Rossi, Joaquin Vanschoren, Bernd Bischl, André C. P. L. F. de Carvalho
2015 — conf
Towards a Collaborative Platform for Advanced Meta-Learning in Healthcare Predictive Analytics.
MetaSel@PKDD/ECML
Milan Vukicevic, Sandro Radovanovic, Joaquin Vanschoren, Giulio Napolitano, Boris Delibasic
2015 B conf
Who is More Positive in Private? Analyzing Sentiment Differences across Privacy Levels and Demographic Factors in Facebook Chats and Posts.
ASONAM
Bo Gao, Bettina Berendt, Joaquin Vanschoren
2014 B conf
Algorithm Selection on Data Streams.
Discovery Science
Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren
2014 J jnl
Open science in machine learning.
CoRR
Joaquin Vanschoren, Mikio L. Braun, Cheng Soon Ong
2014 J jnl
OpenML: networked science in machine learning.
CoRR
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luís Torgo
2014 — ed.
Proceedings of the International Workshop on Meta-learning and Algorithm Selection co-located with 21st European Conference on Artificial Intelligence, MetaSel@ECAI 2014, Prague, Czech Republic, August 19, 2014.
MetaSel@ECAI
Joaquin Vanschoren, Pavel Brazdil, Carlos Soares, Lars Kotthoff
2014 — conf
Towards Meta-learning over Data Streams.
MetaSel@ECAI
Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren
2013 J jnl
A survey of intelligent assistants for data analysis.
ACM Comput. Surv.
Floarea Serban, Joaquin Vanschoren, Jörg-Uwe Kietz, Abraham Bernstein
2013 — conf
OpenML: A Collaborative Science Platform.
ECML/PKDD (3)
Jan N. van Rijn, Bernd Bischl, Luís Torgo, Bo Gao, Venkatesh Umaashankar, Simon Fischer, Patrick Winter, Bernd Wiswedel, Michael R. Berthold, Joaquin Vanschoren
2013 J jnl
OpenML: networked science in machine learning.
SIGKDD Explor.
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luís Torgo
2012 J jnl
Experiment databases - A new way to share, organize and learn from experiments.
Mach. Learn.
Joaquin Vanschoren, Hendrik Blockeel, Bernhard Pfahringer, Geoffrey Holmes
2012 — conf
MDL-Based Analysis of Time Series at Multiple Time-Scales.
ECML/PKDD (2)
Ugo Vespier, Arno J. Knobbe, Siegfried Nijssen, Joaquin Vanschoren
2012 — conf
Scientific Workflow Management with ADAMS.
ECML/PKDD (2)
Peter Reutemann, Joaquin Vanschoren
2012 — conf
Selecting Classification Algorithms with Active Testing.
MLDM
Rui Leite, Pavel Brazdil, Joaquin Vanschoren
2011 — ch.
Datenanalyse und -visualisierung.
Handbuch Forschungsdatenmanagement
Bettina Berendt, Joaquin Vanschoren, Bo Gao
2011 — ch.
Meta-Learning Architectures: Collecting, Organizing and Exploiting Meta-Knowledge.
Meta-Learning in Computational Intelligence
Joaquin Vanschoren
2011 B conf
Traffic Events Modeling for Structural Health Monitoring.
IDA
Ugo Vespier, Arno J. Knobbe, Joaquin Vanschoren, Shengfa Miao, Arne Koopman, Bas Obladen, Carlos Bosma
2010 — ch.
Experiment Databases.
Inductive Databases and Constraint-Based Data Mining
Joaquin Vanschoren, Hendrik Blockeel
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
2009 — conf
A Community-Based Platform for Machine Learning Experimentation.
ECML/PKDD (2)
Joaquin Vanschoren, Hendrik Blockeel
2008 B conf
Learning from the Past with Experiment Databases.
PRICAI
Joaquin Vanschoren, Bernhard Pfahringer, Geoffrey Holmes
2008 C conf
Organizing the World's Machine Learning Information.
ISoLA
Joaquin Vanschoren, Hendrik Blockeel, Bernhard Pfahringer, Geoffrey Holmes
2007 — conf
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning.
PKDD
Hendrik Blockeel, Joaquin Vanschoren
2007 — conf
Investigating Classifier Learning Behavior with Experiment Databases.
GfKl
Joaquin Vanschoren, Hendrik Blockeel