Astral Space
Convex Analysis at Infinity
$127.50
- Hardcover
656 pages
- Release Date
1 December 2026
Summary
From three of today’s top researchers in machine learning, a groundbreaking new theory for understanding convex minimization at infinity
Numerous fields of study rely on methods for minimizing convex functions. Not all convex functions, however, have finite minimizers; some can only be minimized by a sequence as it heads to infinity, making it considerably more challenging to prove correct convergence to a minimizer.
This book develops an expansive new theory …
Book Details
| ISBN-13: | 9780691261126 |
|---|---|
| ISBN-10: | 0691261121 |
| Author: | Robert E. Schapire, Miroslav Dudík, Matus Telgarsky |
| Publisher: | Princeton University Press |
| Imprint: | Princeton University Press |
| Format: | Hardcover |
| Number of Pages: | 656 |
| Release Date: | 1 December 2026 |
| Dimensions: | 254mm x 178mm |
Robert E. Schapire
Miroslav Dudk is senior principal research manager at Microsoft Research in New York City and cofounder of Fairlearn, an open-source project for algorithmic fairness.
Robert E. Schapire is partner researcher at Microsoft Research in New York City and the coauthor (with Yoav Freund) of Boosting: Foundations and Algorithms.
Matus Telgarsky is associate professor at the Courant Institute School of Mathematics, Computing, and Data Science at New York University.
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