Artificial intelligence and statistics

By Bin Yu, Karl Kumbier. 2017

182 citations recorded by the source corpus.

Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during the generation of data, the development of algorithms, and the evaluation of results. This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population, question of interest, representativeness of training data, and scrutiny of results (PQRS). The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and researches. These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results. We discuss the use of these principles in the contexts of self-driving cars, automated medical diagnoses, and examples from the authors’ collaborative research.

Subfields: Geometry and Topology

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In Geometry and Topology

1. Jian Peng
2. Jing Jiang
3. Xiao-Ming Wu
4. Jiaqi Guan
5. Guodong Long
6. Shirui Pan
7. Qimai Li
8. Qiang Liu

Citations in the corpus

• Adversarially Regularized Graph Autoencoder (2018, background): “RTM [ Chang and Blei, 2009 ] learns the topic distributions of each document from both text and citation.”
• Energy-efficient Amortized Inference with Cascaded Deep Classifiers (2017, methodology): “During the training phase, we adopt the standard data augmentation procedure [Lee et al., 2015; He et al., 2016a] on first three datasets: padding each side of the images by four zeros and randomly cropping a 32x32 image; randomly flipping left to right.”
• Attributed Graph Clustering via Adaptive Graph Convolution (2019, methodology): “Some methods model the interaction between graph connectivity and node features with generative models [Chang and Blei, 2009; Yang et al., 2013; He et al., 2017; Bojchevski and Günnemann, 2018].”