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Editors: Isabelle Guyon, Marius Lindauer, Mihaela van der Schaar, Frank Hutter, Roman Garnett
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Non-Uniform Adversarially Robust Pruning
; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:1/1-16
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso
Kenan Šehić, Alexandre Gramfort, Joseph Salmon, Luigi Nardi; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:2/1-24
YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization
Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:3/1-39
AutoCoG: A Unified Data-Model Co-Search Framework for Graph Neural Networks
Duc N.M Hoang, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:4/1-16
Automated Super-Network Generation for Scalable Neural Architecture Search
Juan Pablo Munoz, Nikolay Lyalyushkin, Chaunte Willetta Lacewell, Anastasia Senina, Daniel Cummings, Anthony Sarah, Alexander Kozlov, Nilesh Jain; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:5/1-15
Opening the Black Box: Automated Software Analysis for Algorithm Selection
Damir Pulatov, Marie Anastacio, Lars Kotthoff, Holger Hoos; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:6/1-18
Automatic Termination for Hyperparameter Optimization
Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias Seeger, Cedric Archambeau; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:7/1-21
Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization
Xingyou Song, Sagi Perel, Chansoo Lee, Greg Kochanski, Daniel Golovin; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:8/1-17
Tackling Neural Architecture Search With Quality Diversity Optimization
Lennart Schneider, Florian Pfisterer, Paul Kent, Juergen Branke, Bernd Bischl, Janek Thomas; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:9/1-30
A Tree-Structured Multi-Task Model Recommender
Lijun Zhang, Xiao Liu, Hui Guan; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:10/1-12
BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoML
Mehdi Bahrami, Wei-Peng Chen, Lei Liu, Mukul Prasad; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:11/1-26
On the Optimality Gap of Warm-Started Hyperparameter Optimization
Parikshit Ram; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:12/1-14
What to expect of hardware metric predictors in NAS
Kevin Alexander Laube, Maximus Mutschler, Andreas Zell; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:13/1-15
Bayesian Generational Population-Based Training
Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael Osborne; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:14/1-27
ScaleNAS: Multi-Path One-Shot NAS for Scale-Aware High-Resolution Representation
Hsin-Pai Cheng, Feng Liang, Meng Li, Bowen Cheng, Feng Yan, Hai Li, Vikas Chandra, Yiran Chen; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:15/1-18
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research
David Salinas, Matthias Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cedric Archambeau; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:16/1-23
DIFER: Differentiable Automated Feature Engineering
Guanghui Zhu, Zhuoer Xu, Chunfeng Yuan, Yihua Huang; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:17/1-17
When, where, and how to add new neurons to ANNs
Kaitlin Maile, Emmanuel Rachelson, Hervé Luga, Dennis George Wilson; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:18/1-12
Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-Learning
Trapit Bansal, Salaheddin Alzubi, Tong Wang, Jay-Yoon Lee, Andrew McCallum; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:19/1-18
Differentiable Architecture Search for Reinforcement Learning
Yingjie Miao, Xingyou Song, John D Co-Reyes, Daiyi Peng, Summer Yue, Eugene Brevdo, Aleksandra Faust; Proceedings of the First International Conference on Automated Machine Learning, PMLR 188:20/1-17
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