事实上，使用machine learning（或者nonparametric estimator）进行估计的o(1/n1/4)收敛速度以及使用sample ...
Double Machine LearningGradient BoostingPerformance-Matched Discretionary AccrualsIn this paper, we study the double machine learning (DML) approach of Chernozhukovet al. (2018) for estimating average treatment effect and apply this approachdoi:10.2139/ssrn.3351314Yang, Jui-Chung...
Hetergeneous Treatment Effect旨在量化实验对不同人群的差异影响,进而通过人群定向/数值策略的方式进行差异化实验,或者对实验进行调整。Double Machine Learning把Treatment作为特征,通过估计特征对目标的影响来计算实验的差异效果。 Machine
摘要: In this paper, we study the double machine learning (DML) approach of Chernozhukovet al. (2018) for estimating average treatment effect and apply this approach关键词: Audit Quality Average Treatment Effect Big N Effect Double Machine Learning Gradient Boosting Performance-Matched Discretionary ...
Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these methods automatically deliver good estimators of causal parameters. Examples of such parameters include individual regression coefficien...
Due to the large space of possible compounds, machine learning methods are used to classify materials as potential photovoltaic absorbers using data from the periodic table, eliminating wasteful computation. A random forest algorithm achieves a cross‐validation accuracy of 86.4% on the constructed data...
--In this paper, we propose a new type of information- theoretic method for the self-organizing maps (SOM), taking into account competition between competitive (output) neurons as well as input neurons. The method is called "double competition", as it considers competition between outputs as ...
Language:All DebeshJha/2020-CBMS-DoubleU-Net Star239 Code Issues Pull requests Official implementation of DoubleU-Net for Semantic Image Segmentation in TensorFlow & Pytorch (Nominated for Best Paper Award (IEEE CBMS)) deep-learningmedical-imagingconvolutional-neural-networksimage-segmentationunetsemantic...
Additional Information Supplementary information accompanies this paper at http://www.nature.com/srep Competing financial interests: The authors declare no competing financial interests. How to cite this article: Pilania, G. et al. Machine learning bandgaps of double perovskites. Sci. Rep. 6, ...
Syrgkanis. Orthogonal Statistical Learning. Proceedings of the 32nd Annual Conference on Learning Theory (COLT), 2019. (Best Paper Award) M. Oprescu, V. Syrgkanis and Z. S. Wu. Orthogonal Random Forest for Causal Inference. Proceedings of the 36th International Conference on Machine Learning (...