Abstract is missing.
- Computationally Efficient High-Dimensional Bayesian Optimization via Variable SelectionYihang Shen, Carl Kingsford. [doi]
- MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization ContextsDiederick Vermetten, Furong Ye, Thomas Bäck, Carola Doerr. [doi]
- CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable FailureLennart Oswald Purucker, Joeran Beel. [doi]
- Learning Activation Functions for Sparse Neural NetworksMohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer. [doi]
- PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box OptimizationAna Kostovska, Gjorgjina Cenikj, Diederick Vermetten, Anja Jankovic 0001, Ana Nikolikj, Urban Skvorc, Peter Korosec, Carola Doerr, Tome Eftimov. [doi]
- MEOW - Multi-Objective Evolutionary Weapon DetectionDaniel Dimanov, Colin Singleton, Shahin Rostami, Emili Balaguer-Ballester. [doi]
- "No Free Lunch" in Neural Architectures? A Joint Analysis of Expressivity, Convergence, and GeneralizationWuyang Chen, Wei Huang, Zhangyang Wang. [doi]
- Meta-Learning for Fast Model Recommendation in Unsupervised Multivariate Time Series Anomaly DetectionJosé Manuel Navarro, Alexis Huet, Dario Rossi 0001. [doi]
- AutoGluon-TimeSeries: AutoML for Probabilistic Time Series ForecastingOleksandr Shchur, Ali Caner Türkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Bernie Wang 0001. [doi]
- Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoMLLennart Oswald Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger H. Hoos. [doi]
- Optimal Resource Allocation for Early Stopping-based Neural Architecture Search MethodsMarcel Aach, Eray Inanc, Rakesh Sarma, Morris Riedel, Andreas Lintermann. [doi]
- Exploiting Network Compressibility and Topology in Zero-Cost NASLichuan Xiang, Rosco Hunter, Minghao Xu, Lukasz Dudziak, Hongkai Wen 0001. [doi]
- Searching for Fairer Machine Learning EnsemblesMichael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar. [doi]
- ABLATOR: Robust Horizontal-Scaling of Machine Learning Ablation ExperimentsIordanis Fostiropoulos, Laurent Itti. [doi]
- Self-Adjusting Weighted Expected Improvement for Bayesian OptimizationCarolin Benjamins, Elena Raponi, Anja Jankovic 0001, Carola Doerr, Marius Lindauer. [doi]
- AlphaD3M: An Open-Source AutoML Library for Multiple ML TasksRoque Lopez, Raoni Lourenço, Rémi Rampin, Sonia Castelo, Aécio S. R. Santos, Jorge Henrique Piazentin Ono, Cláudio T. Silva, Juliana Freire. [doi]
- AutoRL Hyperparameter LandscapesAditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer. [doi]
- Symbolic Explanations for Hyperparameter OptimizationSarah Segel, Helena Graf, Alexander Tornede, Bernd Bischl, Marius Lindauer. [doi]
- Multi-Predict: Few Shot Predictors For Efficient Neural Architecture SearchYash Akhauri, Mohamed S. Abdelfattah. [doi]
- Balanced Mixture of Supernets for Learning the CNN Pooling ArchitectureMehraveh Javan Roshtkhari, Matthew Toews, Marco Pedersoli. [doi]
- Better Practices for Domain AdaptationLinus Ericsson, Da Li 0001, Timothy M. Hospedales. [doi]
- Cost-Effective Hyperparameter Optimization for Large Language Model Generation InferenceChi Wang 0001, Xueqing Liu 0001, Ahmed Hassan Awadallah. [doi]
- Neural Architecture Search for Visual Anomaly SegmentationTommie Kerssies, Joaquin Vanschoren. [doi]
- Poisson Process for Bayesian OptimizationXiaoxing Wang, Jiaxing Li, Chao Xue, Wei Liu, Weifeng Liu, Xiaokang Yang, Junchi Yan, Dacheng Tao. [doi]