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ORCIDAutoML 2023: Potsdam, Germany
- Aleksandra Faust, Roman Garnett, Colin White, Frank Hutter, Jacob R. Gardner:
International Conference on Automated Machine Learning, 12-15 November 2023, Hasso Plattner Institute, Potsdam, Germany. Proceedings of Machine Learning Research 224, PMLR 2023 - Lennart Oswald Purucker, Joeran Beel:
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure. 1/1-23 - Sarah Segel, Helena Graf, Alexander Tornede, Bernd Bischl, Marius Lindauer:
Symbolic Explanations for Hyperparameter Optimization. 2/1-22 - Xiaoxing Wang, Jiaxing Li, Chao Xue, Wei Liu, Weifeng Liu, Xiaokang Yang, Junchi Yan, Dacheng Tao:
Poisson Process for Bayesian Optimization. 3/1-20 - Linus Ericsson, Da Li, Timothy M. Hospedales:
Better Practices for Domain Adaptation. 4/1-25 - Daniel Dimanov, Colin Singleton, Shahin Rostami, Emili Balaguer-Ballester:
MEOW - Multi-Objective Evolutionary Weapon Detection. 5/1-20 - Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer:
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. 6/1-50 - Diederick Vermetten, Furong Ye, Thomas Bäck, Carola Doerr:
MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts. 7/1-14 - Mehraveh Javan Roshtkhari, Matthew Toews, Marco Pedersoli:
Balanced Mixture of Supernets for Learning the CNN Pooling Architecture. 8/1-23 - Oleksandr Shchur, Ali Caner Türkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Bernie Wang:
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting. 9/1-21 - Lennart Oswald Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger H. Hoos:
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML. 10/1-34 - Ana Kostovska, Gjorgjina Cenikj, Diederick Vermetten, Anja Jankovic, Ana Nikolikj, Urban Skvorc, Peter Korosec, Carola Doerr, Tome Eftimov:
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization. 11/1-17 - Marcel Aach, Eray Inanc, Rakesh Sarma, Morris Riedel, Andreas Lintermann:
Optimal Resource Allocation for Early Stopping-based Neural Architecture Search Methods. 12/1-17 - Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer:
AutoRL Hyperparameter Landscapes. 13/1-27 - Wuyang Chen, Wei Huang, Zhangyang Wang:
"No Free Lunch" in Neural Architectures? A Joint Analysis of Expressivity, Convergence, and Generalization. 14/1-29 - Yihang Shen, Carl Kingsford:
Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection. 15/1-27 - Mohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer:
Learning Activation Functions for Sparse Neural Networks. 16/1-19 - Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar:
Searching for Fairer Machine Learning Ensembles. 17/1-19 - Lichuan Xiang, Rosco Hunter, Minghao Xu, Lukasz Dudziak, Hongkai Wen:
Exploiting Network Compressibility and Topology in Zero-Cost NAS. 18/1-14 - Iordanis Fostiropoulos, Laurent Itti:
ABLATOR: Robust Horizontal-Scaling of Machine Learning Ablation Experiments. 19/1-15 - Tommie Kerssies, Joaquin Vanschoren:
Neural Architecture Search for Visual Anomaly Segmentation. 20/1-14 - Chi Wang, Xueqing Liu, Ahmed Hassan Awadallah:
Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference. 21/1-17 - Roque Lopez, Raoni Lourenço, Rémi Rampin, Sonia Castelo, Aécio S. R. Santos, Jorge Henrique Piazentin Ono, Cláudio T. Silva, Juliana Freire:
AlphaD3M: An Open-Source AutoML Library for Multiple ML Tasks. 22/1-22 - Yash Akhauri, Mohamed S. Abdelfattah:
Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Search. 23/1-23 - José Manuel Navarro, Alexis Huet, Dario Rossi:
Meta-Learning for Fast Model Recommendation in Unsupervised Multivariate Time Series Anomaly Detection. 24/1-19
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