Data science and optimization / Sanjeena Dang, Antoine Deza, Swati Gupta, Paul D. McNicholas, Sebastian Pokutta, Masashi Sugiyama, Editors.

Format:

Book

Contributor:

Subedi, Sanjeena, editor.

Deza, Antoine, 1966- editor.

Gupta, Swati, editor.

McNicholas, Paul D., editor.

Pokutta, Sebastian, 1980- editor.

Sugiyama, Masashi, 1974- editor.

Fields Institute for Research in Mathematical Sciences

Series:

Fields Institute communications ; 91.

Fields Institute communications, 1069-5265 ; 91

Language:

English

Subjects (All):

Data structures (Computer science).

Information theory.

Physical Description:

viii, 342 pages : illustrations ; 25 cm.

Place of Publication:

Cham, Switzerland : Springer ; [2026]

Summary:

Data science and optimization are increasingly intertwined as both focus on developing computational and methodological approaches to tackling large and otherwise complex datasets. Optimization is primarily concerned with accuracy, computational efficiency, and robustness while data science emphasizes achieving effective results on real datasets. Although some data science approaches involve the implicit optimization of objective functions, there remains a dearth of work that brings advanced optimization techniques to bear on data science problems. The goal of the Fields Focus Program on Data Science and Optimization held in November 2019 at the Fields Institute in Toronto, was to bring together researchers in data science and optimization, both theoretical and applied, in an effort to bridge the fields and stimulate cross-disciplinary interaction and collaboration. In the spirit of the program, this volume compiles recent development and connections in the fields of data science and optimization, and the ways in which they overlap. It features novel results and state-of-the-art surveys as well as open problems.

Contents:

A General Algorithm for Assortment Optimization under Random Utility Choice Models / Tien Mai, Andrea Lodi

Design of Poisoning Attacks on Linear Regression Using Bilevel Optimization / Zeynep Şuvak, Miguel F. Anjos, Luce Brotcorne, Diego Cattaruzza

1-Norm Minimization and Minimum-Rank Structured Sparsity for Symmetric and Ah-symmetric Generalized Inverses: Rank One and Two / Luze Xu, Marcia Fampa, Jon Lee

Local and Global Uniform Convexity Conditions / Thomas Kerdreux, Alexandre d’ Aspremont, Sebastian Pokutta

A Symmetric Loss Perspective of Reliable Machine Learning / Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama

Decoding Noisy Messages: A Method That Just Shouldn’t Work / Leo Liberti

On Reduction of the Switching Graph Problem to the Independent Set Problem / Yotaro Takazawa, Shinji Mizuno

Outer Approximations of Core Points for Integer Programming / Naghmeh Shahverdizadeh, Seyyedemahsa Banihashemi, David Bremner

Sizing the White Whale / Antoine Deza, Mingfei Hao, Lionel Pournin

Too Many Fairness Metrics: Is There a Solution? Equity Across Demographic Groups for the Facility Location Problem / Swati Gupta, Akhil Jalan, Gireeja Ranade, Helen Yang, Simon Zhuang

Adaptive First- and Second-Order Algorithms for Large-Scale Machine Learning / Sanae Lotfi, Tiphaine Bonniot de Ruisselet, Dominique Orban, Andrea Lodi

Second-Order Conditional Gradient Sliding / Alejandro Carderera, Sebastian Pokutta

Combinatorial Pure Exploration with Full-Bandit Feedback and Beyond: Solving Combinatorial Optimization Under Uncertainty with Limited Observation / Yuko Kuroki, Junya Honda, Masashi Sugiyama.

ISBN:

303203843X

9783032038432

OCLC:

1528937451