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A Framework and Benchmark for Deep Batch Active Learning for Regression

, , , and . Journal of Machine Learning Research, 24 (164): 1--81 (2023)

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Best-scored Random Forest Classification., , and . CoRR, (2019)A Framework and Benchmark for Deep Batch Active Learning for Regression, , , and . J. Mach. Learn. Res., 24 (164): 1–81 (2023)liquidSVM : a fast and versatile SVM package, and . Preprint, (2017)Fast learning from α-mixing observations, and . Journal of multivariate analysis, (2014)Oracle inequalities for support vector machines that are based on random entropy numbers. Journal of complexity, 25 (5): 437-454 (2009)On robustness properties of convex risk minimization methods for pattern recognition, and . Journal of machine learning research, 5 (August): 1007-1034 (2004)QP algorithms with guaranteed accuracy and run time for support vector machine, , , and . Journal of machine learning research, 7 (May): 733-769 (2006)Sparseness of support vector machines. Journal of machine learning research, 4 (November): 1071-1105 (2003)Optimal regression rates for SVMs using Gaussian kernels, and . Electronic journal of statistics, (2013)Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments, , , and . Software, (2021)Related to: V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,” J. Chem. Theory Comput. 17, 6658–6670 (2021). doi: 10.1021/acs.jctc.1c00527.