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[AUTOML23] Towards Green Automated Machine Learning: Status Quo and Future Directions
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1 year ago
[AUTOML23] Towards Green Automated Machine Learning: Status Quo and Future Directions
![[AUTOML23] MEOW - Multi-Objective Evolutionary Weapon Detection Teaser](http://i.ytimg.com/vi/p4Qh52Pc3Vw/mqdefault.jpg "[AUTOML23] MEOW - Multi-Objective Evolutionary Weapon Detection Teaser")
![[AUTOML23] On the selection of neural architectures from a supernet](http://i.ytimg.com/vi/RlN7495cbbQ/mqdefault.jpg "[AUTOML23] On the selection of neural architectures from a supernet")
![[AUTOML23] Towards Green Automated Machine Learning: Status Quo and Future Directions](http://i.ytimg.com/vi/7XPDdTf94u8/mqdefault.jpg "[AUTOML23] Towards Green Automated Machine Learning: Status Quo and Future Directions")
![[AUTOML23] Towards Green Automated Machine Learning: Status Quo and Future Directions Teaser](http://i.ytimg.com/vi/Ek7ggUZfQ_Q/mqdefault.jpg "[AUTOML23] Towards Green Automated Machine Learning: Status Quo and Future Directions Teaser")
![[AUTOML23] On the selection of neural architectures from a supernet Teaser](http://i.ytimg.com/vi/Vn5iWrgF1nA/mqdefault.jpg "[AUTOML23] On the selection of neural architectures from a supernet Teaser")
![[AUTOML23] ABLATOR: Robust Horizontal-Scaling of Machine Learning Ablation Experiments](http://i.ytimg.com/vi/90vAEdJ3xE4/mqdefault.jpg "[AUTOML23] ABLATOR: Robust Horizontal-Scaling of Machine Learning Ablation Experiments")
![[AUTOML23] forester: A Novel Approach to Accessible and Interpretable AutoML for Tree-Based Teaser](http://i.ytimg.com/vi/_tu5exPy_JE/mqdefault.jpg "[AUTOML23] forester: A Novel Approach to Accessible and Interpretable AutoML for Tree-Based Teaser")
![[AUTOML23] Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective](http://i.ytimg.com/vi/ZWOQ7eSwUU4/mqdefault.jpg "[AUTOML23] Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective")
![[AUTOML23] Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Object Teaser](http://i.ytimg.com/vi/S55ml9kS8gI/mqdefault.jpg "[AUTOML23] Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Object Teaser")
![[AUTOML23] Balanced Mixture of Supernets for Learning the CNN Pooling Architecture](http://i.ytimg.com/vi/pyHIR4qBm7o/mqdefault.jpg "[AUTOML23] Balanced Mixture of Supernets for Learning the CNN Pooling Architecture")
![[AUTOML23] Learning Debuggable Models Through Multi-Objective Neural Architecture Search](http://i.ytimg.com/vi/L6xZmPU5q7M/mqdefault.jpg "[AUTOML23] Learning Debuggable Models Through Multi-Objective Neural Architecture Search")
![[AUTOML23] CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure Teaser](http://i.ytimg.com/vi/ejAIstHl_SY/mqdefault.jpg "[AUTOML23] CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure Teaser")
![[AUTOML23] SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](http://i.ytimg.com/vi/-qdiIRXT-14/mqdefault.jpg "[AUTOML23] SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization")