Foundations of Data Science

By Avrim Blum, John E. Hopcroft, R. Kannan. 2020

387 citations recorded by the source corpus.

Computer science as an academic discipline began in the 1960’s. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical computer science covered finite automata, regular expressions, context-free languages, and computability. In the 1970’s, the study of algorithms was added as an important component of theory. The emphasis was on making computers useful. Today, a fundamental change is taking place and the focus is more on applications. There are many reasons for this change. The merging of computing and communications has played an important role. The enhanced ability to observe, collect, and store data in the natural sciences, in commerce, and in other fields calls for a change in our understanding of data and how to handle it in the modern setting. The emergence of the web and social networks as central aspects of daily life presents both opportunities and challenges for theory.

Subfields: Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Signal Processing

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Citations in the corpus

Concept whitening for interpretable image recognition (2020, background): “Fortunately, due to properties of high dimensional geometry (Blum et al., 2016), if our data are whitened (standardized and decorrelated), there is some hope that the concepts can be fully represented by unit vectors.”
Boosting Variational Inference: an Optimization Perspective (2017, background): “Note that K depends implicitly on d and it decreases whenever d increases and the support of A remains fixed [5].”
InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs (2020, background): “Theorem 2 (Gaussian Annulus Theorem [70]) For a d-dimensional spherical Gaussian with unit variance in each direction, for any β ≤ √ d, all but at most 3e−cβ 2 of the probability mass lies within the annulus √ d − β ≤ ||z||2 ≤ √ d+ β, where c is a fixed positive constant.”
Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach (2019, background): “line of best fit) from linear regression [15].”