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Gourab Ray

dblp:184/2346 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0000-2579-7077ORCID · reported

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
Information theory · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information theory › signal processing
boundary detection
0.912025
Sharp Signal Detection Under Ferromagnetic Ising Models · IEEE Trans. Inf. Theory 2025
Information theory
hypothesis testing
0.912025
Sharp Signal Detection Under Ferromagnetic Ising Models · IEEE Trans. Inf. Theory 2025
Information theory › hypothesis testing
signal detection
0.912025
Sharp Signal Detection Under Ferromagnetic Ising Models · IEEE Trans. Inf. Theory 2025

Methods — techniques the papers use, named apart from their topics

moderate deviation bounds · 0.9
YearPublicationVenuePosition
2025 Sharp Signal Detection Under Ferromagnetic Ising Models
abstract
In this paper, we study a structured signal detection problem in Ferromagnetic Ising models with examples encompassing Ising Models on lattices, and Mean-Field type Ising Models such as dense Erdős-Rényi, and dense random regular graphs. We provide sharp constants of detection in each of these cases and thereby pinpoint an asymptotically precise relationship between the detection problem with the underlying dependence. To obtain this sharp characterization of the detection boundary at the level of sharp multiplicative constants, we derive necessary moderate deviation bounds for partial summands of magnetizations which might be of independent interest. Finally, we demonstrate how our tests can be designed to be adaptive over the strength of dependence present in the respective models.
Sohom Bhattacharya, Rajarshi Mukherjee, Gourab Ray
IEEE Trans. Inf. Theory3