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OpenML: networked science in machine learning

29 July 2014

Jan N. van Rijn

Papers citing "OpenML: networked science in machine learning"

20 / 20 papers shown

Title
Autoencoding Random Forests Binh Duc Vu Jan Kapar Marvin N. Wright David S. Watson 143 0 0 27 May 2025
OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization Meher Bhaskar Madiraju Meher Sai Preetam Madiraju 133 0 0 25 May 2025
Evolving Machine Learning: A Survey Ignacio Cabrera Martin Subhaditya Mukherjee Almas Baimagambetov Joaquin Vanschoren Nikolaos Polatidis VLM 233 0 0 23 May 2025
CLIMB: Class-imbalanced Learning Benchmark on Tabular Data Zhining Liu Zihao Li Ze Yang Tianxin Wei Jian Kang Yada Zhu Hendrik Hamann Jingrui He Hanghang Tong LMTD 169 0 0 23 May 2025
Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation D. Sculley Will Cukierski Phil Culliton Sohier Dane Maggie Demkin ... Addison Howard Paul Mooney Walter Reade Megan Risdal Nate Keating 91 2 0 01 May 2025
AutoML Benchmark with shorter time constraints and early stopping Israel Campero Jurado Pieter Gijsbers Joaquin Vanschoren AI4TS 374 0 0 01 Apr 2025
MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model Alexander Koebler Ingo Thon Florian Buettner 55 0 0 26 Mar 2025
EDCA - An Evolutionary Data-Centric AutoML Framework for Efficient Pipelines Joana Simões João Correia 449 0 0 06 Mar 2025
A Comprehensive Study of Shapley Value in Data Analytics Hong Lin Shixin Wan Zhongle Xie Ke Chen Meihui Zhang Lidan Shou Gang Chen 141 0 0 02 Dec 2024
Sequential Large Language Model-Based Hyper-parameter Optimization Kanan Mahammadli Seyda Ertekin 211 5 0 27 Oct 2024
Efficient Optimization Algorithms for Linear Adversarial Training Antônio H. Ribeiro Thomas B. Schon Dave Zahariah Francis Bach AAML 76 2 0 16 Oct 2024
Targeted synthetic data generation for tabular data via hardness characterization Tommaso Ferracci Leonie Goldmann Anton Hinel Francesco Sanna Passino 244 1 0 01 Oct 2024
DCA-Bench: A Benchmark for Dataset Curation Agents Benhao Huang Yingzhuo Yu Jin Huang Xingjian Zhang Jiaqi Ma 87 1 0 11 Jun 2024
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients Evandro S. Ortigossa Fábio F. Dias Brian Barr Claudio T. Silva L. G. Nonato FAtt 118 3 0 25 Apr 2024
Assessing the Use of AutoML for Data-Driven Software Engineering Fabio Calefato L. Quaranta F. Lanubile Marcos Kalinowski 74 7 0 20 Jul 2023
A Framework and Benchmark for Deep Batch Active Learning for Regression David Holzmüller Viktor Zaverkin Johannes Kastner Ingo Steinwart UQCV BDL GP 92 37 0 17 Mar 2022
Adaptation Strategies for Automated Machine Learning on Evolving Data B. Celik Joaquin Vanschoren 50 56 0 09 Jun 2020
Large-scale benchmark study of survival prediction methods using multi-omics data Moritz Herrmann Philipp Probst R. Hornung V. Jurinovic A. Boulesteix 52 78 0 07 Mar 2020
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition Vincent Dorie J. Hill Uri Shalit M. Scott D. Cervone CML 185 288 0 09 Jul 2017
To tune or not to tune the number of trees in random forest? Philipp Probst A. Boulesteix 64 391 0 16 May 2017