EDBT 2026 Demo / reviewers in the wild / expert
Alon Zakai
dblp:66/2708
· DBLP profile ↗
5ranked-venue papers
2as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 4 · 2 first-authorSoftware engineering, systems software and programming languages · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Programming languages and type systems · 56% Compilers and program optimization · 44% | |
| Artificial intelligence
4 papers |
Representation and self-supervised learning · 31% Learning theory · 30% Kernel, tree and ensemble methods · 24% | |
| Network and information security
1 paper |
Web and mobile security · 100% |
Topics — the 8 heaviest of 12, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Compilers and program optimization
compilation target |
0.3 | 1 | 2017 | Bringing the web up to speed with WebAssembly · PLDI 2017 |
Programming languages and type systems
webassembly |
0.3 | 1 | 2017 | Bringing the web up to speed with WebAssembly · PLDI 2017 |
Programming languages and type systems
language design |
0.1 | 1 | 2017 | Bringing the web up to speed with WebAssembly · PLDI 2017 |
Machine learning › Representation and self-supervised learning › representation learning
dimensionality reduction |
0.1 | 1 | 2008 | Manifold Learning: The Price of Normalization · J. Mach. Learn. Res. 2008 |
Machine learning › Deep learning architectures and training › neural network training
local learning |
0.1 | 1 | 2008 | How Local Should a Learning Method Be? · COLT 2008 |
Machine learning › Representation and self-supervised learning › representation learning › dimensionality reduction
manifold learning |
0.1 | 1 | 2008 | Manifold Learning: The Price of Normalization · J. Mach. Learn. Res. 2008 |
Machine learning › Kernel, tree and ensemble methods › ensemble learning
boosting |
0.1 | 1 | 2006 | Some Theory for Generalized Boosting Algorithms · J. Mach. Learn. Res. 2006 |
Machine learning › Kernel, tree and ensemble methods
ensemble learning |
0.1 | 1 | 2006 | Some Theory for Generalized Boosting Algorithms · J. Mach. Learn. Res. 2006 |
Methods — techniques the papers use, named apart from their topics
spectral methods · 0.1normalization · 0.1local learning · 0.1hilbert space · 0.1gauss-southwell optimization · 0.1convergence analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Bringing the web up to speed with WebAssemblyabstractThe maturation of the Web platform has given rise to sophisticated and demanding Web applications such as interactive 3D visualization, audio and video software, and games. With that, efficiency and security of code on the Web has become more important than ever. Yet JavaScript as the only built-in language of the Web is not well-equipped to meet these requirements, especially as a compilation target. Andreas Haas, Andreas Rossberg, Derek L. Schuff, Ben L. Titzer, Michael Holman, Dan Gohman, Luke Wagner, Alon Zakai, J. F. Bastien |
PLDI | 8 |
| 2009 | Consistency and Localizability
Alon Zakai, Yaacov Ritov |
J. Mach. Learn. Res. | 1 |
| 2008 | How Local Should a Learning Method Be?
Alon Zakai, Yaacov Ritov |
COLT | 1 |
| 2008 | Manifold Learning: The Price of Normalization
Yair Goldberg, Alon Zakai, Dan Kushnir, Yaacov Ritov |
J. Mach. Learn. Res. | 2 |
| 2006 | Some Theory for Generalized Boosting AlgorithmsabstractWe give a review of various aspects of boosting, clarifying the issues through a few simple results, and relate our work and that of others to the minimax paradigm of statistics. We consider the population version of the boosting algorithm and prove its convergence to the Bayes classifier as a corollary of a general result about Gauss-Southwell optimization in Hilbert space. We then investigate the algorithmic convergence of the sample version, and give bounds to the time until perfect separation of the sample. We conclude by some results on the statistical optimality of the L2 boosting. Peter J. Bickel, Yaacov Ritov, Alon Zakai |
J. Mach. Learn. Res. | 3 |