Demo Data from EDBT 2026 / reviewers in the wild / expert
Nitzan Tur
dblp:238/8258
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2025
0000-0001-8810-4581ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 2 · 2 since 2021
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 |
Approximation and online algorithms · 50% Mathematical optimization · 50% | |
| Network and information security
1 paper |
Privacy and data protection · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Privacy and data protection
differential privacy |
0.9 | 1 | 2025 | On Differentially Private Linear Algebra · STOC 2025 |
Approximation and online algorithms › approximation algorithms › approximation algorithms for graph problems
0-extension |
0.5 | 1 | 2021 | The metric relaxation for 0-extension admits an Ω(log2/3k) gap · STOC 2021 |
Approximation and online algorithms
approximation algorithms |
0.5 | 1 | 2021 | The metric relaxation for 0-extension admits an Ω(log2/3k) gap · STOC 2021 |
Mathematical optimization › linear programming relaxation
integrality gap |
0.5 | 1 | 2021 | The metric relaxation for 0-extension admits an Ω(log2/3k) gap · STOC 2021 |
Mathematical optimization
linear programming relaxation |
0.5 | 1 | 2021 | The metric relaxation for 0-extension admits an Ω(log2/3k) gap · STOC 2021 |
Methods — techniques the papers use, named apart from their topics
differential privacy · 0.9metric relaxation · 0.5algebraic topology · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | On Differentially Private Linear Algebra
Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer, Nitzan Tur |
STOC | 5 |
| 2021 | The metric relaxation for 0-extension admits an Ω(log2/3k) gapabstractWe consider the 0-Extension problem, where we are given an undirected graph G=(V,E) equipped with non-negative edge weights w:E→ ℝ+, a collection T={ t1,…,tk}⊆ V of k special vertices called terminals, and a semi-metric D over T. The goal is to assign every non-terminal vertex to a terminal while minimizing the sum over all edges of the weight of the edge multiplied by the distance in D between the terminals to which the endpoints of the edge are assigned. 0-Extension admits two known algorithms, achieving approximations of O(logk) [Călinescu-Karloff-Rabani SICOMP ’05] and O(logk/loglogk) [Fakcharoenphol-Harrelson-Rao-Talwar SODA ’03]. Both known algorithms are based on rounding a natural linear programming relaxation called the metric relaxation, in which D is extended from T to the entire of V. The current best known integrality gap for the metric relaxation is Ω (√logk). In this work we present an improved integrality gap of Ω(log2/3k) for the metric relaxation. Our construction is based on the randomized extension of one graph by another, a notion that captures lifts of graphs as a special case and might be of independent interest. Inspired by algebraic topology, our analysis of the gap instance is based on proving no continuous section (in the topological sense) exists in the randomized extension. Roy Schwartz 0002, Nitzan Tur |
STOC | 2 |