Demo Data from EDBT 2026 / reviewers in the wild / expert
Keren Cohavi
dblp:149/2354
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1 · 1 first-author
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.
| Theoretical computer science
1 paper |
Combinatorics and discrete mathematics · 38% Algorithms and data structures · 38% Mathematical optimization · 23% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithms and data structures
sketching |
0.3 | 1 | 2017 | Faster and Simpler Sketches of Valuation Functions · ACM Trans. Algorithms 2017 |
Combinatorics and discrete mathematics › set functions
submodular functions |
0.3 | 1 | 2017 | Faster and Simpler Sketches of Valuation Functions · ACM Trans. Algorithms 2017 |
Mathematical optimization › continuous optimization
convex optimization |
0.1 | 1 | 2017 | Faster and Simpler Sketches of Valuation Functions · ACM Trans. Algorithms 2017 |
Mathematical optimization › linear programming
ellipsoid method |
0.1 | 1 | 2017 | Faster and Simpler Sketches of Valuation Functions · ACM Trans. Algorithms 2017 |
Methods — techniques the papers use, named apart from their topics
value queries · 0.3demand queries · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Faster and Simpler Sketches of Valuation FunctionsabstractWe present fast algorithms for sketching valuation functions. Let N (| N | = n ) be some ground set and v :2 N → R be a function. We say that v˜:2 N → R is an α-sketch of v if for every set S we have that v ( S )/α ≤ v˜( S ) ≤ v ( S ) and v˜ can be described in poly ( n ) bits. Goemans et al. [SODA’09] showed that if v is submodular then there exists an õ (√ n )-sketch that can be constructed using polynomially many value queries (this is essentially the best possible, as Balcan and Harvey [STOC’11] show that no submodular function admits an n 1/3 - ϵ -sketch). Based on their work, Balcan et al. [COLT’12] and Badanidiyuru et al. [SODA’12] show that if v is subadditive, then there exists an õ (√ n )-sketch that can be constructed using polynomially many demand queries. All previous sketches are based on complicated geometric constructions. The first step in their constructions is proving the existence of a good sketch by finding an ellipsoid that “approximates” v well (this is done by applying John’s theorem to ensure the existence of an ellipsoid that is “close” to the polymatroid that is associated with v ). The second step is to show that this ellipsoid can be found efficiently, and this is done by repeatedly solving a certain convex program to obtain better approximations of John’s ellipsoid. In this article, we give a significantly simpler, nongeometric proof for the existence of good sketches and utilize the proof to obtain much faster algorithms that match the previously obtained approximation bounds. Specifically, we provide an algorithm that finds õ (√ n )-sketch of a submodular function with only õ ( n 3/2 value queries, and we provide an algorithm that finds õ (√ n )-sketch of a subadditive function with O ( n ) demand and value queries. Keren Cohavi, Shahar Dobzinski |
ACM Trans. Algorithms | 1 |