EDBT 2026 Demo / reviewers in the wild / expert
Benjamin Arras
dblp:186/7883
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2018
0000-0001-9067-1163ORCID · reported
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 · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information theory › information measures
fisher information |
0.3 | 1 | 2018 | IT Formulae for Gamma Target: Mutual Information and Relative Entropy · IEEE Trans. Inf. Theory 2018 |
Information theory › information measures › divergence measures
kullback-leibler divergence |
0.3 | 1 | 2018 | IT Formulae for Gamma Target: Mutual Information and Relative Entropy · IEEE Trans. Inf. Theory 2018 |
Information theory › information measures
mutual information |
0.3 | 1 | 2018 | IT Formulae for Gamma Target: Mutual Information and Relative Entropy · IEEE Trans. Inf. Theory 2018 |
Methods — techniques the papers use, named apart from their topics
stochastic representation · 0.3de bruijn identity · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | IT Formulae for Gamma Target: Mutual Information and Relative EntropyabstractIn this paper, we introduce new Stein identities for gamma target distribution as well as a new non-linear channel specifically designed for gamma inputs. From these two ingredients, we derive an explicit and simple formula for the derivative of the input-output mutual information of this non-linear channel with respect to the channel quality parameter. This relation is reminiscent of the well-known link between the derivative of the input-output mutual information of additive Gaussian noise channel with respect to the signal-to-noise ratio and the minimum mean-square error. The proof relies on a rescaled version of De Bruijn identity for gamma target distribution together with a stochastic representation for the gamma-specific Fisher information. Finally, we are able to derive precise bounds and asymptotics for the input-output mutual information of the non-linear channel with gamma inputs. Benjamin Arras, Yvik Swan |
IEEE Trans. Inf. Theory | 1 |