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ORCIDAutoML 2024: Paris, France
- Katharina Eggensperger, Roman Garnett, Joaquin Vanschoren, Marius Lindauer, Jacob R. Gardner:
International Conference on Automated Machine Learning, 9-12 September 2024, Sorbonne Université, Paris, France. Proceedings of Machine Learning Research 256, PMLR 2024 - Riccardo Grazzi, Julien Niklas Siems, Simon Schrodi, Thomas Brox, Frank Hutter:
Is Mamba Capable of In-Context Learning? 1/1-26 - Yue Zhao, Leman Akoglu:
HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection. 2/1-24 - Vishak Prasad C., Colin White, Sibasis Nayak, Paarth Jain, Aziz Shameem, Prateek Garg, Ganesh Ramakrishnan:
Speeding up NAS with Adaptive Subset Selection. 3/1-23 - Konstantinos Paraschakis, Andrea Castellani, Giorgos Borboudakis, Ioannis Tsamardinos:
Confidence Interval Estimation of Predictive Performance in the Context of AutoML. 4/1-14 - Timotée Ly-Manson, Mathieu Léonardon, Abdeldjalil Aïssa-El-Bey, Ghouthi Boukli Hacene, Lukas Mauch:
Analyzing Few-Shot Neural Architecture Search in a Metric-Driven Framework. 5/1-33 - Jordan Dotzel, Gang Wu, Andrew Li, Muhammad Umar, Yun Ni, Mohamed S. Abdelfattah, Zhiru Zhang, Liqun Cheng, Martin G. Dixon, Norman P. Jouppi, Quoc V. Le, Sheng Li:
FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search. 6/1-26 - Adri Gomez Martin, Mónica Abella, Manuel Desco:
Improving Transfer Learning by means of Ensemble Learning and Swarm Intelligence-based Neuroevolution. 7/1-25 - Mateo Avila Pava, René Groh, Andreas M. Kist:
Sequence Alignment-based Similarity Metric in Evolutionary Neural Architecture Search. 8/1-21 - Edward Bergman, Lennart Purucker, Frank Hutter:
Don't Waste Your Time: Early Stopping Cross-Validation. 9/1-31 - Jonas Seng, Fabian Kalter, Zhongjie Yu, Fabrizio Ventola, Kristian Kersting:
Bi-Level One-Shot Architecture Search for Probabilistic Time Series Forecasting. 10/1-20 - Nilesh Verma, Albert Bifet, Bernhard Pfahringer, Maroua Bahri:
ASML: A Scalable and Efficient AutoML Solution for Data Streams. 11/1-26 - Rhea Sanjay Sukthanker, Arjun Krishnakumar, Mahmoud Safari, Frank Hutter:
Weight-Entanglement Meets Gradient-Based Neural Architecture Search. 12/1-25 - Martin Hirzel, Kiran Kate, Louis Mandel, Avraham Shinnar:
Training and Cross-Validating Machine Learning Pipelines with Limited Memory. 13/1-25 - Shuhei Watanabe, Neeratyoy Mallik, Edward Bergman, Frank Hutter:
Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks. 14/1-18 - Zhiqiang Tang, Haoyang Fang, Su Zhou, Taojiannan Yang, Zihan Zhong, Cuixiong Hu, Katrin Kirchhoff, George Karypis:
AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models. 15/1-35 - Julie Keisler, Sandra Claudel, Gilles Cabriel, Margaux Brégère:
Automated Deep Learning for load forecasting. 16/1-28 - Luca Piras, Joan Albert Erráez Castelltort, Jordi Casals Grifell, Xavier de Juan Pulido, Cirus Iniesta, Marina Rosell Murillo, Cristina Soler Arenys:
Introducing HoNCAML: Holistic No-Code Auto Machine Learning. 17/1-27 - Gresa Shala, Sebastian Pineda-Arango, André Biedenkapp, Frank Hutter, Josif Grabocka:
HPO-RL-Bench: A Zero-Cost Benchmark for HPO in Reinforcement Learning. 18/1-31 - David Salinas, Nick Erickson:
TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications. 19/1-30
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