The Algorithmic Dimension
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The Algorithmic Dimension
Fifty years after the first experiments in computational art, international interest in the history of this subject remains strong and at the same time almost uncovered. This book began with the exhibition Algorithmic Signs, which was conceived, researched and curated by Francesca Franco in Venice in 2017. The origins of the exhibition included a series of meetings that gathered together the most celebrated international pioneers in the world of digital arts and the rare opportunity to interview them in their studios. Francesca Franco explores the history of computer art and its contribution to the broader field of contemporary art from the 1960s to the present. It is illustrated by the creative work of five of the most influential pioneers of computer art - Ernest Edmonds, Manfred Mohr, Vera Molnar, Frieder Nake, and Roman Verostko and includes the full visual documentation of the exhibition. The Algorithmic Dimension - Five Artists in Conversation offers more than a theoretical perspective; it offers readers the rare opportunity to hear the histories and developments of the fascinating art, created through the algorithm, in an accessible and stimulating narrative. The personal achievements of each artist are followed, including their original inspirations, and how they develop in parallel with technological advances. It also brings together for the first time the artists' common ideas and differences, and tales about how their paths have crossed over the years.
Handbook of Computability and Complexity in Analysis
Computable analysis is the modern theory of computability and complexity in analysis that arose out of Turing's seminal work in the 1930s. This was motivated by questions such as: which real numbers and real number functions are computable, and which mathematical tasks in analysis can be solved by algorithmic means? Nowadays this theory has many different facets that embrace topics from computability theory, algorithmic randomness, computational complexity, dynamical systems, fractals, and analog computers, up to logic, descriptive set theory, constructivism, and reverse mathematics. In recent decades computable analysis has invaded many branches of analysis, and researchers have studied computability and complexity questions arising from real and complex analysis, functional analysis, and the theory of differential equations, up to (geometric) measure theory and topology. This handbook represents the first coherent cross-section through most active research topics on the more theoretical side of the field. It contains 11 chapters grouped into parts on computability in analysis; complexity, dynamics, and randomness; and constructivity, logic, and descriptive complexity. All chapters are written by leading experts working at the cutting edge of the respective topic. Researchers and graduate students in the areas of theoretical computer science and mathematical logic will find systematic introductions into many branches of computable analysis, and a wealth of information and references that will help them to navigate the modern research literature in this field.
Algorithmic High-Dimensional Robust Statistics
Author: Ilias Diakonikolas
language: en
Publisher: Cambridge University Press
Release Date: 2023-09-07
Robust statistics is the study of designing estimators that perform well even when the dataset significantly deviates from the idealized modeling assumptions, such as in the presence of model misspecification or adversarial outliers in the dataset. The classical statistical theory, dating back to pioneering works by Tukey and Huber, characterizes the information-theoretic limits of robust estimation for most common problems. A recent line of work in computer science gave the first computationally efficient robust estimators in high dimensions for a range of learning tasks. This reference text for graduate students, researchers, and professionals in machine learning theory, provides an overview of recent developments in algorithmic high-dimensional robust statistics, presenting the underlying ideas in a clear and unified manner, while leveraging new perspectives on the developed techniques to provide streamlined proofs of these results. The most basic and illustrative results are analyzed in each chapter, while more tangential developments are explored in the exercises.