Demo Data from VLDB 2026 / reviewers in the wild / expert
Kabir Peshawaria
dblp:420/0956
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1 · 1 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 |
Computational complexity · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational complexity › property testing
boolean function testing |
1.0 | 1 | 2026 | Relative-Error Unateness Testing · ICALP 2026 |
Computational complexity
lower bounds |
1.0 | 1 | 2026 | Relative-Error Unateness Testing · ICALP 2026 |
Computational complexity
property testing |
1.0 | 1 | 2026 | Relative-Error Unateness Testing · ICALP 2026 |
Computational complexity › property testing
property testing lower bounds |
1.0 | 1 | 2026 | Relative-Error Unateness Testing · ICALP 2026 |
Computational complexity › property testing › boolean function testing
unateness testing |
1.0 | 1 | 2026 | Relative-Error Unateness Testing · ICALP 2026 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Relative-Error Unateness TestingabstractThe model of relative-error property testing of Boolean functions has been the subject of significant recent research effort [X. Chen et al., 2025; Chen et al., 2025; Chen et al., 2025]. In this paper we consider the problem of relative-error testing an unknown and arbitrary f: {0,1}ⁿ → {0,1} for the property of being a unate function, i.e. a function that is either monotone non-increasing or monotone non-decreasing in each of the n input variables. Our first result is a one-sided non-adaptive algorithm for this problem that makes Õ(log(N)/ε) samples and queries, where N = |f^{-1}(1)| is the number of satisfying assignments of the function that is being tested and the value of N is given as an input parameter to the algorithm. Building on this algorithm, we next give a one-sided adaptive algorithm for this problem that does not need to be given the value of N and with high probability makes Õ(log(N)/ε) samples and queries. We also give lower bounds for both adaptive and non-adaptive two-sided algorithms that are given the value of N up to a constant multiplicative factor. In the non-adaptive case, our lower bounds essentially match the complexity of the algorithm that we provide. Xi Chen 0001, Diptaksho Palit, Kabir Peshawaria, William Pires, Rocco A. Servedio |
ICALP | 3 |