Joaquin Vanschoren

ORCID

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 102

Top Venues

Katholieke Universiteit Leuven, Belgium (PHD)

Education History

Katholieke Universiteit Leuven, Belgium

PHD

Publications

Prabhant Singh, Pieter Gijsbers, Murat Onur Yildirim, Elif Ceren Gok, Joaquin Vanschoren
CLAMS: A System for Zero-Shot Model Selection for Clustering. CoRR (2024)
Anna Vettoruzzo, Lorenzo Braccaioli, Joaquin Vanschoren, Marlena Nowaczyk
Unsupervised Meta-Learning via In-Context Learning. CoRR (2024)
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) (2024)
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 (2024)
Anvitha Thirthapura Sreedhara, Joaquin Vanschoren
Can time series forecasting be automated? A benchmark and analysis. CoRR (2024)
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 (2024)
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) (2024)
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 (2024)
Prabhant Singh, Joaquin Vanschoren
Robust and Efficient Transfer Learning via Supernet Transfer in Warm-started Neural Architecture Search. CoRR (2024)
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) (2024)
Branislav Pecher, Ivan Srba, Mária Bieliková, Joaquin Vanschoren
Automatic Combination of Sample Selection Strategies for Few-Shot Learning. CoRR (2024)
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)
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 (2024)
Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren, Ran Piao
HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis. CoRR (2024)
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 (2024)
Bilge Celik, Prabhant Singh, Joaquin Vanschoren
Online AutoML: an adaptive AutoML framework for online learning. Mach. Learn. 112 (6) (2023)
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 (2023)
Israel Campero Jurado, Joaquin Vanschoren
An Analysis of Evolutionary Migration Models for Multi-Objective, Multi-Fidelity Automl. SMC (2023)
Mert Kilickaya, Joaquin Vanschoren
Are Labels Needed for Incremental Instance Learning? CVPR Workshops (2023)
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 (2023)
Elif Ceren Gok Yildirim, Murat Onur Yildirim, Mert Kilickaya, Joaquin Vanschoren
Adaptive Regularization for Class-Incremental Learning. CoRR (2023)
Thomas Boot, Nicolas Cazin, Willem P. Sanberg, Joaquin Vanschoren
Efficient-DASH: Automated Radar Neural Network Design Across Tasks and Datasets. IV (2023)
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)
Mert Kilickaya, Joaquin Vanschoren
What Can AutoML Do For Continual Learning? CoRR (2023)
Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren
Locality-Aware Hyperspectral Classification. BMVC (2023)
Mert Kilickaya, Joaquin Vanschoren
Are Labels Needed for Incremental Instance Learning? CoRR (2023)
Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren
Locality-Aware Hyperspectral Classification. CoRR (2023)
Prabhant Singh, Joaquin Vanschoren
AutoML for Outlier Detection with Optimal Transport Distances. IJCAI (2023)
Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Joaquin Vanschoren, Thorsteinn S. Rögnvaldsson, KC Santosh
Advances and Challenges in Meta-Learning: A Technical Review. CoRR (2023)
Tommie Kerssies, Joaquin Vanschoren
Neural Architecture Search for Visual Anomaly Segmentation. AutoML (2023)
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 D. Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson
DMLR: Data-centric Machine Learning Research - Past, Present and Future. CoRR (2023)
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 (2023)
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) (2023)
Jiarong Pan, Stefan Falkner, Felix Berkenkamp, Joaquin Vanschoren
MALIBO: Meta-learning for Likelihood-free Bayesian Optimization. CoRR (2023)
Adrian El Baz, Isabelle Guyon, Zhengying Liu, Jan N. van Rijn, Sébastien Treguer, Joaquin Vanschoren
Advances in MetaDL: AAAI 2021 challenge and workshop. CoRR (2022)
Prabhant Singh, Joaquin Vanschoren
Meta-Learning for Unsupervised Outlier Detection with Optimal Transport. CoRR (2022)
G. G. H. Franken, Prabhant Singh, Joaquin Vanschoren
EmProx: Neural Network Performance Estimation For Neural Architecture Search. CoRR (2022)
Prabhant Singh, Joaquin Vanschoren
Automated Imbalanced Learning. CoRR (2022)
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 (2022)
Tommie Kerssies, Joaquin Vanschoren, Mert Kiliçkaya
Evaluating Continual Test-Time Adaptation for Contextual and Semantic Domain Shifts. CoRR (2022)
Reza Refaei Afshar, Yingqian Zhang, Joaquin Vanschoren, Uzay Kaymak
Automated Reinforcement Learning: An Overview. CoRR (2022)
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) (2022)
Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren
AMLB: an AutoML Benchmark. CoRR (2022)
Bilge Celik, Prabhant Singh, Joaquin Vanschoren
Online AutoML: An adaptive AutoML framework for online learning. CoRR (2022)
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) (2022)
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 (2022)
Gideon Franken, Prabhant Singh, Joaquin Vanschoren
Faster Performance Estimation for NAS with Embedding Proximity Score. Meta-Knowledge Transfer @ ECML/PKDD (2022)
Israel Campero Jurado, Andrejs Fedjajevs, Joaquin Vanschoren, Aarnout Brombacher
Interpretable Assessment of ST-Segment Deviation in ECG Time Series. Sensors 22 (13) (2022)
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)
Fangqin Zhou, Joaquin Vanschoren
Open-Ended Learning Strategies for Learning Complex Locomotion Skills. CoRR (2022)
Israel Campero-Jurado, Joaquin Vanschoren
Multi-fidelity optimization method with asynchronous generalized island model for AutoML. GECCO Companion (2022)
Matej Grobelnik, Joaquin Vanschoren
Warm-starting DARTS using meta-learning. CoRR (2022)
Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren
Meta-Learning for Symbolic Hyperparameter Defaults. CoRR (2021)
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)
Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren
Meta-learning for symbolic hyperparameter defaults. GECCO Companion (2021)
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)
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 (2021)
Joaquin Vanschoren
Towards Scalable Online Machine Learning Collaborations with OpenML. Proc. VLDB Endow. 14 (13) (2021)
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)
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 (2021)
John W. van Lith, Joaquin Vanschoren
From Strings to Data Science: a Practical Framework for Automated String Handling. CoRR (2021)
Bilge Celik, Joaquin Vanschoren
Adaptation Strategies for Automated Machine Learning on Evolving Data. IEEE Trans. Pattern Anal. Mach. Intell. 43 (9) (2021)
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 (2021)
Mikhail Evchenko, Joaquin Vanschoren, Holger H. Hoos, Marc Schoenauer, Michèle Sebag
Frugal Machine Learning. CoRR (2021)
Jeroen van Hoof, Joaquin Vanschoren
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models. CoRR (2021)
Rishabh Goyal, Joaquin Vanschoren, Victor van Acht, Stephan Nijssen
Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms. CoRR (2021)
Larisa N. Soldatova, Joaquin Vanschoren
Guest editors' introduction to the special issue on Discovery Science. Mach. Learn. 109 (11) (2020)
Michael R. Heffels, Joaquin Vanschoren
Aerial Imagery Pixel-level Segmentation. CoRR (2020)
Bilge Celik, Joaquin Vanschoren
Adaptation Strategies for Automated Machine Learning on Evolving Data. CoRR (2020)
Pieter Gijsbers, Joaquin Vanschoren
GAMA: a General Automated Machine learning Assistant. CoRR (2020)
Pieter Gijsbers, Joaquin Vanschoren
GAMA: A General Automated Machine Learning Assistant. ECML/PKDD (5) (2020)
Hilde J. P. Weerts, Andreas C. Mueller, Joaquin Vanschoren
Importance of Tuning Hyperparameters of Machine Learning Algorithms. CoRR (2020)
Maarten Grootendorst, Joaquin Vanschoren
Beyond Bag-of-Concepts: Vectors of Locally Aggregated Concepts. ECML/PKDD (2) (2019)
Pieter Gijsbers, Erin LeDell, Janek Thomas, Sébastien Poirier, Bernd Bischl, Joaquin Vanschoren
An Open Source AutoML Benchmark. CoRR (2019)
J. Gomez Robles, Joaquin Vanschoren
Learning to reinforcement learn for Neural Architecture Search. CoRR (2019)
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 (2019)
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) (2019)
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) (2019)
Pieter Gijsbers, Joaquin Vanschoren
GAMA: Genetic Automated Machine learning Assistant. J. Open Source Softw. 4 (33) (2019)
Laurens A. Castelijns, Yuri Maas, Joaquin Vanschoren
The ABC of Data: A Classifying Framework for Data Readiness. PKDD/ECML Workshops (1) (2019)
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 (2019)
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 (2019)
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric 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 R. 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 (2019)
Yezi Zhu, Marc Aoun, Marcel Krijn, Joaquin Vanschoren
Data Augmentation using Conditional Generative Adversarial Networks for Leaf Counting in Arabidopsis Plants. BMVC (2018)
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) (2018)
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) (2018)
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 (2018)
Joaquin Vanschoren
Meta-Learning: A Survey. CoRR (2018)
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 (2018)
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 (2018)
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) (2018)
Pieter Gijsbers, Joaquin Vanschoren, Randal S. Olson
Layered TPOT: Speeding up Tree-based Pipeline Optimization. CoRR (2018)
Iván Olier, Oghenejokpeme I. Orhobor, Joaquin Vanschoren, Ross D. King
Transformative Machine Learning. CoRR (2018)
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 (2017)
Pieter Gijsbers, Joaquin Vanschoren, Randal S. Olson
Layered TPOT: Speeding up Tree-based Pipeline Optimization. AutoML@PKDD/ECML (2017)
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 (2017)
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 (2017)
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 (2016)
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)
Agnieszka Lawrynowicz, Diego Esteves, Pance Panov, Tommaso Soru, Saso Dzeroski, Joaquin Vanschoren
An Algorithm, Implementation and Execution Ontology Design Pattern. WOP@ISWC (2016)

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Publication Activity (10 Years)

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Joaquin Vanschoren