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AutoML 2022: Baltimore, MD, USA
- Isabelle Guyon, Marius Lindauer, Mihaela van der Schaar, Frank Hutter, Roman Garnett:
International Conference on Automated Machine Learning, AutoML 2022, 25-27 July 2022, Johns Hopkins University, Baltimore, MD, USA. Proceedings of Machine Learning Research 188, PMLR 2022 - Qi Zhao, Tim Köonigl, Christian Wressnegger:
Non-Uniform Adversarially Robust Pruning. 1/1-16 - Kenan Sehic, Alexandre Gramfort, Joseph Salmon, Luigi Nardi:
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso. 2/1-24 - Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl:
YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. 3/1-39 - Duc N. M. Hoang, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang:
AutoCoG: A Unified Data-Model Co-Search Framework for Graph Neural Networks. 4/1-16 - Juan Pablo Muñoz, Nikolay Lyalyushkin, Chaunte Willetta Lacewell, Anastasia Senina, Daniel Cummings, Anthony Sarah, Alexander Kozlov, Nilesh Jain:
Automated Super-Network Generation for Scalable Neural Architecture Search. 5/1-15 - Damir Pulatov, Marie Anastacio, Lars Kotthoff, Holger H. Hoos:
Opening the Black Box: Automated Software Analysis for Algorithm Selection. 6/1-18 - Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau:
Automatic Termination for Hyperparameter Optimization. 7/1-21 - Xingyou Song, Sagi Perel, Chansoo Lee, Greg Kochanski, Daniel Golovin:
Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization. 8/1-17 - Lennart Schneider, Florian Pfisterer, Paul Kent, Jürgen Branke, Bernd Bischl, Janek Thomas:
Tackling Neural Architecture Search With Quality Diversity Optimization. 9/1-30 - Lijun Zhang, Xiao Liu, Hui Guan:
A Tree-Structured Multi-Task Model Recommender. 10/1-12 - Mehdi Bahrami, Wei-Peng Chen, Lei Liu, Mukul R. Prasad:
BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoML. 11/1-26 - Parikshit Ram:
On the Optimality Gap of Warm-Started Hyperparameter Optimization. 12/1-14 - Kevin Alexander Laube, Maximus Mutschler, Andreas Zell:
What to expect of hardware metric predictors in NAS. 13/1-15 - Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael A. Osborne:
Bayesian Generational Population-Based Training. 14/1-27 - Hsin-Pai Cheng, Feng Liang, Meng Li, Bowen Cheng, Feng Yan, Hai Li, Vikas Chandra, Yiran Chen:
ScaleNAS: Multi-Path One-Shot NAS for Scale-Aware High-Resolution Representation. 15/1-18 - David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau:
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. 16/1-23 - Guanghui Zhu, Zhuoer Xu, Chunfeng Yuan, Yihua Huang:
DIFER: Differentiable Automated Feature Engineering. 17/1-17 - Kaitlin Maile, Emmanuel Rachelson, Hervé Luga, Dennis George Wilson:
When, where, and how to add new neurons to ANNs. 18/1-12 - Trapit Bansal, Salaheddin Alzubi, Tong Wang, Jay-Yoon Lee, Andrew McCallum:
Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-Learning. 19/1-18 - Yingjie Miao, Xingyou Song, John D. Co-Reyes, Daiyi Peng, Summer Yue, Eugene Brevdo, Aleksandra Faust:
Differentiable Architecture Search for Reinforcement Learning. 20/1-17
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