Demo Data from EDBT 2026 / reviewers in the wild / expert
D. W. Sauder
dblp:11/2466
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1994
—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 |
Information theory · 54% Algorithmic game theory and mechanism design · 23% Mathematical optimization · 23% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information theory › channel capacity › input constraints
power constraints |
0.0 | 1 | 1994 | Signal detection games with power constraints · IEEE Trans. Inf. Theory 1994 |
Mathematical optimization › minimax optimization
saddle point |
0.0 | 1 | 1994 | Signal detection games with power constraints · IEEE Trans. Inf. Theory 1994 |
Information theory › hypothesis testing
signal detection |
0.0 | 1 | 1994 | Signal detection games with power constraints · IEEE Trans. Inf. Theory 1994 |
Algorithmic game theory and mechanism design
zero-sum game |
0.0 | 1 | 1994 | Signal detection games with power constraints · IEEE Trans. Inf. Theory 1994 |
Information theory › hypothesis testing › composite hypothesis testing
minimax detection |
0.0 | 1 | 1994 | Signal detection games with power constraints · IEEE Trans. Inf. Theory 1994 |
Methods — techniques the papers use, named apart from their topics
maximin solution · 0.0game theory · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1994 | Signal detection games with power constraintsabstractFormulates and solves maximin and minimax detection problems for signals with power constraints. These problems arise whenever it is necessary to distinguish between a genuine signal and a spurious one designed by an adversary with the principal goal of deceiving the detector. The spurious (or deceptive) signal is subject to certain constraints, such as limited power, which preclude it from replicating the genuine signal exactly. The detection problem is formulated as a zero-sum game involving two players: the detector designer and the signal designer. The payoff is the probability of error of the detector, which the detector designer tries to minimize and the deceptive signal designer to maximize. For this detection game, saddle point solutions-whenever possible-or otherwise maximin and minimax solutions are derived under three distinct constraints on the deceptive signal power; these distinct constraints involve lower bounds on (i) the signal amplitude, (ii) the time-averaged power, and (iii) the expected power. The cases of independent and identically distributed and correlated signals are considered.> D. W. Sauder, Evaggelos Geraniotis |
IEEE Trans. Inf. Theory | 1 |