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ORCIDJoaquin Vanschoren
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- affiliation: Eindhoven University of Technology, The Netherlands
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2020 – today
- 2024
[j27]Rafael Gomes Mantovani, Tomás Horváth, André L. D. Rossi, Ricardo Cerri, Sylvio Barbon Junior, Joaquin Vanschoren
, André C. P. L. F. de Carvalho:
Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms. Data Min. Knowl. Discov. 38(3): 1364-1416 (2024)- [j26]Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter:
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. J. Artif. Intell. Res. 79: 639-677 (2024) - [j25]Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren:
AMLB: an AutoML Benchmark. J. Mach. Learn. Res. 25: 101:1-101:65 (2024) - [j24]Ambarish Moharil, Joaquin Vanschoren, Prabhant Singh, Damian A. Tamburri:
Towards efficient AutoML: a pipeline synthesis approach leveraging pre-trained transformers for multimodal data. Mach. Learn. 113(9): 7011-7053 (2024) - [j23]Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Joaquin Vanschoren, Thorsteinn S. Rögnvaldsson, KC Santosh:
Advances and Challenges in Meta-Learning: A Technical Review. IEEE Trans. Pattern Anal. Mach. Intell. 46(7): 4763-4779 (2024) - [c49]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:
Croissant: A Metadata Format for ML-Ready Datasets. DEEM@SIGMOD 2024: 1-6 - [c48]Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren, Ran Piao:
HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis. ECCV (32) 2024: 236-252 - [c47]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:
Position: TrustLLM: Trustworthiness in Large Language Models. ICML 2024 - [c46]Jiarong Pan, Stefan Falkner, Felix Berkenkamp, Joaquin Vanschoren:
MALIBO: Meta-learning for Likelihood-free Bayesian Optimization. ICML 2024 - [i61]Branislav Pecher, Ivan Srba, Mária Bieliková, Joaquin Vanschoren:
Automatic Combination of Sample Selection Strategies for Few-Shot Learning. CoRR abs/2402.03038 (2024) - [i60]Murat Onur Yildirim, Elif Ceren Gok Yildirim, Decebal Constantin Mocanu, Joaquin Vanschoren:
FOCIL: Finetune-and-Freeze for Online Class Incremental Learning by Training Randomly Pruned Sparse Experts. CoRR abs/2403.14684 (2024) - [i59]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:
Croissant: A Metadata Format for ML-Ready Datasets. CoRR abs/2403.19546 (2024) - [i58]Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Borhane Blili-Hamelin, Kurt D. 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 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:
Introducing v0.5 of the AI Safety Benchmark from MLCommons. CoRR abs/2404.12241 (2024) - [i57]Anna Vettoruzzo, Lorenzo Braccaioli, Joaquin Vanschoren, Marlena Nowaczyk:
Unsupervised Meta-Learning via In-Context Learning. CoRR abs/2405.16124 (2024) - [i56]Prabhant Singh, Pieter Gijsbers, Murat Onur Yildirim, Elif Ceren Gok, Joaquin Vanschoren:
CLAMS: A System for Zero-Shot Model Selection for Clustering. CoRR abs/2407.11286 (2024) - [i55]Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren, Ran Piao:
HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis. CoRR abs/2407.16269 (2024) - [i54]Anvitha Thirthapura Sreedhara, Joaquin Vanschoren:
Can time series forecasting be automated? A benchmark and analysis. CoRR abs/2407.16445 (2024) - [i53]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:
A Standardized Machine-readable Dataset Documentation Format for Responsible AI. CoRR abs/2407.16883 (2024) - [i52]Prabhant Singh, Joaquin Vanschoren:
Robust and Efficient Transfer Learning via Supernet Transfer in Warm-started Neural Architecture Search. CoRR abs/2407.20279 (2024) - [i51]Anna Vettoruzzo, Joaquin Vanschoren, Mohamed-Rafik Bouguelia, Thorsteinn S. Rögnvaldsson:
Learning to Learn without Forgetting using Attention. CoRR abs/2408.03219 (2024) - [i50]Elif Ceren Gok Yildirim, Murat Onur Yildirim, Joaquin Vanschoren:
Continual Learning on a Data Diet. CoRR abs/2410.17715 (2024) - 2023
- [j22]Bilge Celik, Prabhant Singh, Joaquin Vanschoren:
Online AutoML: an adaptive AutoML framework for online learning. Mach. Learn. 112(6): 1897-1921 (2023) - [j21]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:
Signal Quality Analysis for Long-Term ECG Monitoring Using a Health Patch in Cardiac Patients. Sensors 23(4): 2130 (2023) - [c45]Tommie Kerssies, Joaquin Vanschoren:
Neural Architecture Search for Visual Anomaly Segmentation. AutoML 2023: 20/1-14 - [c44]Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren:
Locality-Aware Hyperspectral Classification. BMVC 2023: 22-24 - [c43]Mert Kilickaya, Joaquin Vanschoren:
Are Labels Needed for Incremental Instance Learning? CVPR Workshops 2023: 2401-2409 - [c42]Prabhant Singh, Joaquin Vanschoren:
AutoML for Outlier Detection with Optimal Transport Distances. IJCAI 2023: 7175-7178 - [c41]Thomas Boot, Nicolas Cazin, Willem P. Sanberg, Joaquin Vanschoren:
Efficient-DASH: Automated Radar Neural Network Design Across Tasks and Datasets. IV 2023: 1-7 - [c40]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:
DataPerf: Benchmarks for Data-Centric AI Development. NeurIPS 2023 - [c39]Israel Campero-Jurado, Joaquin Vanschoren:
An Analysis of Evolutionary Migration Models for Multi-Objective, Multi-Fidelity Automl. SMC 2023: 2940-2945 - [i49]Mert Kilickaya, Joaquin Vanschoren:
Are Labels Needed for Incremental Instance Learning? CoRR abs/2301.11417 (2023) - [i48]Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N. van Rijn, Haozhe Sun, Joaquin Vanschoren, Phan Anh Vu:
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification. CoRR abs/2302.08909 (2023) - [i47]Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter:
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. CoRR abs/2303.08485 (2023) - [i46]Elif Ceren Gok Yildirim, Murat Onur Yildirim, Mert Kilickaya, Joaquin Vanschoren:
Adaptive Regularization for Class-Incremental Learning. CoRR abs/2303.13113 (2023) - [i45]Jiarong Pan, Stefan Falkner, Felix Berkenkamp, Joaquin Vanschoren:
MALIBO: Meta-learning for Likelihood-free Bayesian Optimization. CoRR abs/2307.03565 (2023) - [i44]Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Joaquin Vanschoren, Thorsteinn S. Rögnvaldsson, KC Santosh:
Advances and Challenges in Meta-Learning: A Technical Review. CoRR abs/2307.04722 (2023) - [i43]Murat Onur Yildirim, Elif Ceren Gok Yildirim, Ghada Sokar, Decebal Constantin Mocanu, Joaquin Vanschoren:
Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates. CoRR abs/2308.14831 (2023) - [i42]Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren:
Locality-Aware Hyperspectral Classification. CoRR abs/2309.01561 (2023) - [i41]Mert Kilickaya, Joaquin Vanschoren:
What Can AutoML Do For Continual Learning? CoRR abs/2311.11963 (2023) - [i40]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 D. Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson:
DMLR: Data-centric Machine Learning Research - Past, Present and Future. CoRR abs/2311.13028 (2023) - 2022
- [j20]Adriano Rivolli, Luís Paulo F. Garcia, Carlos Soares, Joaquin Vanschoren, André C. P. L. F. de Carvalho:
Meta-features for meta-learning. Knowl. Based Syst. 240: 108101 (2022) - [j19]Israel Campero-Jurado, Andrejs Fedjajevs, Joaquin Vanschoren, Aarnout Brombacher:
Interpretable Assessment of ST-Segment Deviation in ECG Time Series. Sensors 22(13): 4919 (2022) - [j18]Jefrey Lijffijt, Dimitra Gkorou, Pieter Van Hertum, Alexander Ypma, Mykola Pechenizkiy, Joaquin Vanschoren:
Introduction to the Special Section on AI in Manufacturing: Current Trends and Challenges. SIGKDD Explor. 24(2): 81-85 (2022) - [j17]Chao Zhang, Joaquin Vanschoren, Arlette van Wissen, Daniël Lakens, Boris E. R. de Ruyter, Wijnand A. IJsselsteijn:
Theory-based habit modeling for enhancing behavior prediction in behavior change support systems. User Model. User Adapt. Interact. 32(3): 389-415 (2022) - [c38]Gideon Franken, Prabhant Singh, Joaquin Vanschoren:
Faster Performance Estimation for NAS with Embedding Proximity Score. Meta-Knowledge Transfer @ ECML/PKDD 2022: 51-61 - [c37]Israel Campero-Jurado, Joaquin Vanschoren:
Multi-fidelity optimization method with asynchronous generalized island model for AutoML. GECCO Companion 2022: 220-223 - [c36]Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N. van Rijn, Haozhe Sun, Joaquin Vanschoren, Phan Anh Vu:
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification. NeurIPS 2022 - [i39]Reza Refaei Afshar, Yingqian Zhang, Joaquin Vanschoren, Uzay Kaymak:
Automated Reinforcement Learning: An Overview. CoRR abs/2201.05000 (2022) - [i38]Bilge Celik, Prabhant Singh, Joaquin Vanschoren:
Online AutoML: An adaptive AutoML framework for online learning. CoRR abs/2201.09750 (2022) - [i37]Adrian El Baz, Isabelle Guyon, Zhengying Liu, Jan N. van Rijn, Sébastien Treguer, Joaquin Vanschoren:
Advances in MetaDL: AAAI 2021 challenge and workshop. CoRR abs/2202.01890 (2022) - [i36]Matej Grobelnik, Joaquin Vanschoren:
Warm-starting DARTS using meta-learning. CoRR abs/2205.06355 (2022) - [i35]G. G. H. Franken, Prabhant Singh, Joaquin Vanschoren:
EmProx: Neural Network Performance Estimation For Neural Architecture Search. CoRR abs/2206.05972 (2022) - [i34]Fangqin Zhou, Joaquin Vanschoren:
Open-Ended Learning Strategies for Learning Complex Locomotion Skills. CoRR abs/2206.06796 (2022) - [i33]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:
DataPerf: Benchmarks for Data-Centric AI Development. CoRR abs/2207.10062 (2022) - [i32]Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren:
AMLB: an AutoML Benchmark. CoRR abs/2207.12560 (2022) - [i31]Tommie Kerssies, Joaquin Vanschoren, Mert Kiliçkaya:
Evaluating Continual Test-Time Adaptation for Contextual and Semantic Domain Shifts. CoRR abs/2208.08767 (2022) - [i30]Prabhant Singh, Joaquin Vanschoren:
Meta-Learning for Unsupervised Outlier Detection with Optimal Transport. CoRR abs/2211.00372 (2022) - [i29]Prabhant Singh, Joaquin Vanschoren:
Automated Imbalanced Learning. CoRR abs/2211.00376 (2022) - 2021
- [j16]Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter:
OpenML-Python: an extensible Python API for OpenML. J. Mach. Learn. Res. 22: 100:1-100:5 (2021) - [j15]Bilge Celik, Joaquin Vanschoren:
Adaptation Strategies for Automated Machine Learning on Evolving Data. IEEE Trans. Pattern Anal. Mach. Intell. 43(9): 3067-3078 (2021) - [j14]Joaquin Vanschoren:
Towards Scalable Online Machine Learning Collaborations with OpenML. Proc. VLDB Endow. 14(13): 3418 (2021) - [c35]Adrian El Baz, Isabelle Guyon, Zhengying Liu, Jan N. van Rijn, Sébastien Treguer, Joaquin Vanschoren:
Advances in MetaDL: AAAI 2021 Challenge and Workshop. MetaDL@AAAI 2021: 1-16 - [c34]Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren:
Meta-learning for symbolic hyperparameter defaults. GECCO Companion 2021: 151-152 - [c33]Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren:
OpenML Benchmarking Suites. NeurIPS Datasets and Benchmarks 2021 - [c32]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:
NeurIPS’22 Cross-Domain MetaDL Challenge: Results and lessons learned. NeurIPS (Competition and Demos) 2021: 50-72 - [e9]Isabelle Guyon, Jan N. van Rijn, Sébastien Treguer, Joaquin Vanschoren:
AAAI Workshop on Meta-Learning and MetaDL Challenge, MetaDL@AAAI 2021, virtual, February 9, 2021. Proceedings of Machine Learning Research 140, PMLR 2021 [contents] - [e8]Joaquin Vanschoren, Sai-Kit Yeung:
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December 2021, virtual. 2021 [contents] - [i28]Chao Zhang, Joaquin Vanschoren, Arlette van Wissen, Daniël Lakens, Boris E. R. de Ruyter, Wijnand A. IJsselsteijn:
Theory-based Habit Modeling for Enhancing Behavior Prediction. CoRR abs/2101.01637 (2021) - [i27]Jeroen van Hoof, Joaquin Vanschoren:
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models. CoRR abs/2101.02289 (2021) - [i26]Rishabh Goyal, Joaquin Vanschoren, Victor van Acht, Stephan Nijssen:
Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms. CoRR abs/2102.02147 (2021) - [i25]Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren:
Meta-Learning for Symbolic Hyperparameter Defaults. CoRR abs/2106.05767 (2021) - [i24]Irma van den Brandt, Floris Fok, Bas Mulders, Joaquin Vanschoren, Veronika Cheplygina:
Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification. CoRR abs/2107.05940 (2021) - [i23]John W. van Lith, Joaquin Vanschoren:
From Strings to Data Science: a Practical Framework for Automated String Handling. CoRR abs/2111.01868 (2021) - [i22]Mikhail Evchenko, Joaquin Vanschoren, Holger H. Hoos, Marc Schoenauer, Michèle Sebag:
Frugal Machine Learning. CoRR abs/2111.03731 (2021) - 2020
- [j13]Larisa N. Soldatova, Joaquin Vanschoren:
Guest editors' introduction to the special issue on Discovery Science. Mach. Learn. 109(11): 1993-1995 (2020) - [c31]Pieter Gijsbers, Joaquin Vanschoren:
GAMA: A General Automated Machine Learning Assistant. ECML/PKDD (5) 2020: 560-564 - [i21]Bilge Celik, Joaquin Vanschoren:
Adaptation Strategies for Automated Machine Learning on Evolving Data. CoRR abs/2006.06480 (2020) - [i20]Pieter Gijsbers, Joaquin Vanschoren:
GAMA: a General Automated Machine learning Assistant. CoRR abs/2007.04911 (2020) - [i19]Hilde J. P. Weerts, Andreas C. Müller, Joaquin Vanschoren:
Importance of Tuning Hyperparameters of Machine Learning Algorithms. CoRR abs/2007.07588 (2020) - [i18]Michael R. Heffels, Joaquin Vanschoren:
Aerial Imagery Pixel-level Segmentation. CoRR abs/2012.02024 (2020)
2010 – 2019
- 2019
- [j12]Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl:
OpenML: An R package to connect to the machine learning platform OpenML. Comput. Stat. 34(3): 977-991 (2019) - [j11]Rafael Gomes Mantovani, André L. D. Rossi, Edesio Alcobaça, Joaquin Vanschoren, André C. P. L. F. de Carvalho:
A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers. Inf. Sci. 501: 193-221 (2019) - [j10]Noureddin Sadawi, Iván Olier, Joaquin Vanschoren, Jan N. van Rijn, Jeremy Besnard, G. Richard J. Bickerton, Crina Grosan, Larisa N. Soldatova, Ross D. King:
Multi-task learning with a natural metric for quantitative structure activity relationship learning. J. Cheminformatics 11(1): 68:1-68:13 (2019) - [j9]Pieter Gijsbers, Joaquin Vanschoren:
GAMA: Genetic Automated Machine learning Assistant. J. Open Source Softw. 4(33): 1132 (2019) - [c30]Laurens A. Castelijns, Yuri Maas, Joaquin Vanschoren:
The ABC of Data: A Classifying Framework for Data Readiness. PKDD/ECML Workshops (1) 2019: 3-16 - [c29]Maarten Grootendorst, Joaquin Vanschoren:
Beyond Bag-of-Concepts: Vectors of Locally Aggregated Concepts. ECML/PKDD (2) 2019: 681-696 - [p4]Joaquin Vanschoren:
Meta-Learning. Automated Machine Learning 2019: 35-61 - [e7]Frank Hutter, Lars Kotthoff, Joaquin Vanschoren:
Automated Machine Learning - Methods, Systems, Challenges. The Springer Series on Challenges in Machine Learning, Springer 2019, ISBN 978-3-030-05317-8 [contents] - [i17]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 G. 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:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i16]Rafael Gomes Mantovani, André Luis Debiaso Rossi, Edesio Alcobaça, Joaquin Vanschoren, André Carlos Ponce de Leon Ferreira de Carvalho:
A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers. CoRR abs/1906.01684 (2019) - [i15]Pieter Gijsbers, Erin LeDell, Janek Thomas, Sébastien Poirier, Bernd Bischl, Joaquin Vanschoren:
An Open Source AutoML Benchmark. CoRR abs/1907.00909 (2019) - [i14]Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter:
OpenML-Python: an extensible Python API for OpenML. CoRR abs/1911.02490 (2019) - [i13]J. Gomez Robles, Joaquin Vanschoren:
Learning to reinforcement learn for Neural Architecture Search. CoRR abs/1911.03769 (2019) - 2018
- [j8]Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N. van Rijn, Joaquin Vanschoren:
Speeding up algorithm selection using average ranking and active testing by introducing runtime. Mach. Learn. 107(1): 79-108 (2018) - [j7]Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren:
The online performance estimation framework: heterogeneous ensemble learning for data streams. Mach. Learn. 107(1): 149-176 (2018) - [j6]Iván Olier, Noureddin Sadawi, G. Richard J. Bickerton, Joaquin Vanschoren, Crina Grosan, Larisa N. Soldatova, Ross D. King:
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery. Mach. Learn. 107(1): 285-311 (2018) - [c28]Yezi Zhu, Marc Aoun, Marcel Krijn, Joaquin Vanschoren:
Data Augmentation using Conditional Generative Adversarial Networks for Leaf Counting in Arabidopsis Plants. BMVC 2018: 324 - [e6]Larisa N. Soldatova, Joaquin Vanschoren, George A. Papadopoulos, Michelangelo Ceci:
Discovery Science - 21st International Conference, DS 2018, Limassol, Cyprus, October 29-31, 2018, Proceedings. Lecture Notes in Computer Science 11198, Springer 2018, ISBN 978-3-030-01770-5 [contents] - [i12]Pieter Gijsbers, Joaquin Vanschoren, Randal S. Olson:
Layered TPOT: Speeding up Tree-based Pipeline Optimization. CoRR abs/1801.06007 (2018) - [i11]Gustavo Correa Publio, Diego Esteves, Agnieszka Lawrynowicz, Pance Panov, Larisa N. Soldatova, Tommaso Soru, Joaquin Vanschoren, Hamid Zafar:
ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies. CoRR abs/1807.05351 (2018) - [i10]Adriano Rivolli, Luís Paulo F. Garcia, Carlos Soares, Joaquin Vanschoren, André C. P. L. F. de Carvalho:
Towards Reproducible Empirical Research in Meta-Learning. CoRR abs/1808.10406 (2018) - [i9]Joaquin Vanschoren:
Meta-Learning: A Survey. CoRR abs/1810.03548 (2018) - [i8]Iván Olier, Oghenejokpeme I. Orhobor, Joaquin Vanschoren, Ross D. King:
Transformative Machine Learning. CoRR abs/1811.03392 (2018) - [i7]Rafael Gomes Mantovani, Tomás Horváth, Ricardo Cerri, Sylvio Barbon Junior, Joaquin Vanschoren, André Carlos Ponce de Leon Ferreira de Carvalho:
An empirical study on hyperparameter tuning of decision trees. CoRR abs/1812.02207 (2018) - 2017
- [c27]Pieter Gijsbers, Joaquin Vanschoren, Randal S. Olson:
Layered TPOT: Speeding up Tree-based Pipeline Optimization. AutoML@PKDD/ECML 2017: 49-68 - [e5]Pavel Brazdil, Joaquin Vanschoren, Frank Hutter, Holger H. Hoos:
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. CEUR Workshop Proceedings 1998, CEUR-WS.org 2017 [contents] - [i6]Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl:
OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML. CoRR abs/1701.01293 (2017) - [i5]Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren:
OpenML Benchmarking Suites and the OpenML100. CoRR abs/1708.03731 (2017) - [i4]Iván Olier, Noureddin Sadawi, G. Richard J. Bickerton, Joaquin Vanschoren, Crina Grosan, Larisa N. Soldatova, Ross D. King:
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery. CoRR abs/1709.03854 (2017) - 2016
- [j5]Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren:
ASlib: A benchmark library for algorithm selection. Artif. Intell. 237: 41-58 (2016) - [j4]Bo Gao, Bettina Berendt, Joaquin Vanschoren:
Toward understanding online sentiment expression: an interdisciplinary approach with subgroup comparison and visualization. Soc. Netw. Anal. Min. 6(1): 68:1-68:16 (2016) - [c26]Rafael Gomes Mantovani, Tomás Horváth, Ricardo Cerri, Joaquin Vanschoren, André C. P. L. F. de Carvalho:
Hyper-Parameter Tuning of a Decision Tree Induction Algorithm. BRACIS 2016: 37-42 - [c25]Chao Zhang, Arlette van Wissen, Daniël Lakens, Joaquin Vanschoren, Boris E. R. de Ruyter, Wijnand A. IJsselsteijn:
Anticipating habit formation: a psychological computing approach to behavior change support. UbiComp Adjunct 2016: 1247-1254 - [c24]Agnieszka Lawrynowicz, Diego Esteves, Pance Panov, Tommaso Soru, Saso Dzeroski, Joaquin Vanschoren:
An Algorithm, Implementation and Execution Ontology Design Pattern. WOP@ISWC 2016: 55-68 - [e4]Frank Hutter, Lars Kotthoff, Joaquin Vanschoren:
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. JMLR Workshop and Conference Proceedings 64, JMLR.org 2016 [contents] - [e3]Paola Festa, Meinolf Sellmann, Joaquin Vanschoren:
Learning and Intelligent Optimization - 10th International Conference, LION 10, Ischia, Italy, May 29 - June 1, 2016, Revised Selected Papers. Lecture Notes in Computer Science 10079, Springer 2016, ISBN 978-3-319-50348-6 [contents] - 2015
- [c23]Bo Gao, Bettina Berendt, Joaquin Vanschoren:
Who is More Positive in Private? Analyzing Sentiment Differences across Privacy Levels and Demographic Factors in Facebook Chats and Posts. ASONAM 2015: 605-610 - [c22]Linda M. Eerikäinen, Joaquin Vanschoren, Michael Johannes Rooijakkers, Rik Vullings, Ronald M. Aarts:
Decreasing the False Alarm Rate of Arrhythmias in Intensive Care Using a Machine Learning Approach. CinC 2015: 293-296 - [c21]Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren:
Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams. ICDM 2015: 1003-1008 - [c20]Jan N. van Rijn, Salisu Mamman Abdulrahman, Pavel Brazdil, Joaquin Vanschoren:
Fast Algorithm Selection Using Learning Curves. IDA 2015: 298-309 - [c19]Rafael Gomes Mantovani, André Luis Debiaso Rossi, Joaquin Vanschoren, Bernd Bischl, André C. P. L. F. de Carvalho:
To tune or not to tune: Recommending when to adjust SVM hyper-parameters via meta-learning. IJCNN 2015: 1-8 - [c18]Rafael Gomes Mantovani, André Luis Debiaso Rossi, Joaquin Vanschoren, Bernd Bischl, André C. P. L. F. de Carvalho:
Effectiveness of Random Search in SVM hyper-parameter tuning. IJCNN 2015: 1-8 - [c17]Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl:
Taking machine learning research online with OpenML. BigMine 2015: 1-4 - [c16]Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N. van Rijn, Joaquin Vanschoren:
Algorithm Selection via Meta-learning and Sample-based Active Testing. MetaSel@PKDD/ECML 2015: 55-66 - [c15]Rafael Gomes Mantovani, André L. D. Rossi, Joaquin Vanschoren, André C. P. L. F. de Carvalho:
Meta-learning Recommendation of Default Hyper-parameter Values for SVMs in Classification Tasks. MetaSel@PKDD/ECML 2015: 80-92 - [c14]Jan N. van Rijn, Joaquin Vanschoren:
Sharing RapidMiner Workflows and Experiments with OpenML. MetaSel@PKDD/ECML 2015: 93-103 - [c13]Milan Vukicevic, Sandro Radovanovic, Joaquin Vanschoren, Giulio Napolitano, Boris Delibasic:
Towards a Collaborative Platform for Advanced Meta-Learning in Healthcare Predictive Analytics. MetaSel@PKDD/ECML 2015: 112-114 - [e2]Joaquin Vanschoren, Pavel Brazdil, Christophe G. Giraud-Carrier, Lars Kotthoff:
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. CEUR Workshop Proceedings 1455, CEUR-WS.org 2015 [contents] - [i3]Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren:
ASlib: A Benchmark Library for Algorithm Selection. CoRR abs/1506.02465 (2015) - 2014
- [c12]Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren:
Algorithm Selection on Data Streams. Discovery Science 2014: 325-336 - [c11]Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren:
Towards Meta-learning over Data Streams. MetaSel@ECAI 2014: 37-38 - [e1]Joaquin Vanschoren, Pavel Brazdil, Carlos Soares, Lars Kotthoff:
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. CEUR Workshop Proceedings 1201, CEUR-WS.org 2014 [contents] - [i2]Joaquin Vanschoren, Mikio L. Braun, Cheng Soon Ong:
Open science in machine learning. CoRR abs/1402.6013 (2014) - [i1]Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luís Torgo:
OpenML: networked science in machine learning. CoRR abs/1407.7722 (2014) - 2013
- [j3]Floarea Serban, Joaquin Vanschoren, Jörg-Uwe Kietz, Abraham Bernstein:
A survey of intelligent assistants for data analysis. ACM Comput. Surv. 45(3): 31:1-31:35 (2013) - [j2]Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luís Torgo:
OpenML: networked science in machine learning. SIGKDD Explor. 15(2): 49-60 (2013) - [c10]Jan N. van Rijn, Bernd Bischl, Luís Torgo, Bo Gao, Venkatesh Umaashankar, Simon Fischer, Patrick Winter, Bernd Wiswedel, Michael R. Berthold, Joaquin Vanschoren:
OpenML: A Collaborative Science Platform. ECML/PKDD (3) 2013: 645-649 - 2012
- [j1]Joaquin Vanschoren, Hendrik Blockeel, Bernhard Pfahringer, Geoffrey Holmes:
Experiment databases - A new way to share, organize and learn from experiments. Mach. Learn. 87(2): 127-158 (2012) - [c9]Rui Leite, Pavel Brazdil, Joaquin Vanschoren:
Selecting Classification Algorithms with Active Testing. MLDM 2012: 117-131 - [c8]Ugo Vespier, Arno J. Knobbe, Siegfried Nijssen, Joaquin Vanschoren:
MDL-Based Analysis of Time Series at Multiple Time-Scales. ECML/PKDD (2) 2012: 371-386 - [c7]Peter Reutemann, Joaquin Vanschoren:
Scientific Workflow Management with ADAMS. ECML/PKDD (2) 2012: 833-837 - 2011
- [c6]Ugo Vespier, Arno J. Knobbe, Joaquin Vanschoren, Shengfa Miao, Arne Koopman, Bas Obladen, Carlos Bosma:
Traffic Events Modeling for Structural Health Monitoring. IDA 2011: 376-387 - [p3]Bettina Berendt, Joaquin Vanschoren, Bo Gao:
Datenanalyse und -visualisierung. Handbuch Forschungsdatenmanagement 2011: 139-148 - [p2]Joaquin Vanschoren:
Meta-Learning Architectures: Collecting, Organizing and Exploiting Meta-Knowledge. Meta-Learning in Computational Intelligence 2011: 117-155 - 2010
- [b1]Joaquin Vanschoren:
Understanding Machine Learning Performance with Experiment Databases (Het verwerven van inzichten in leerperformantie met experiment databanken) ; Understanding Machine Learning Performance with Experiment Databases. Katholieke Universiteit Leuven, Belgium, 2010 - [p1]Joaquin Vanschoren, Hendrik Blockeel:
Experiment Databases. Inductive Databases and Constraint-Based Data Mining 2010: 335-361
2000 – 2009
- 2009
- [c5]Joaquin Vanschoren, Hendrik Blockeel:
A Community-Based Platform for Machine Learning Experimentation. ECML/PKDD (2) 2009: 750-754 - 2008
- [c4]Joaquin Vanschoren, Hendrik Blockeel, Bernhard Pfahringer, Geoffrey Holmes:
Organizing the World's Machine Learning Information. ISoLA 2008: 693-708 - [c3]Joaquin Vanschoren, Bernhard Pfahringer, Geoffrey Holmes:
Learning from the Past with Experiment Databases. PRICAI 2008: 485-496 - 2007
- [c2]Joaquin Vanschoren, Hendrik Blockeel:
Investigating Classifier Learning Behavior with Experiment Databases. GfKl 2007: 421-428 - [c1]Hendrik Blockeel, Joaquin Vanschoren:
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning. PKDD 2007: 6-17
Coauthor Index
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