Demo Data from SIGIR 2026 / reviewers in the wild / expert
Ercai Chen
dblp:198/6466
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—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 |
Information theory · 70% Coding theory · 23% Mathematical optimization · 7% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information theory › information measures
entropy |
0.7 | 1 | 2023 | Some Notes on Variational Principle for Metric Mean Dimension · IEEE Trans. Inf. Theory 2023 |
Information theory › ergodic theory
metric mean dimension |
0.7 | 1 | 2023 | Some Notes on Variational Principle for Metric Mean Dimension · IEEE Trans. Inf. Theory 2023 |
Coding theory › source coding
rate-distortion theory |
0.7 | 1 | 2023 | Some Notes on Variational Principle for Metric Mean Dimension · IEEE Trans. Inf. Theory 2023 |
Information theory › information measures › generalized information measures
rényi information dimension |
0.7 | 1 | 2023 | Some Notes on Variational Principle for Metric Mean Dimension · IEEE Trans. Inf. Theory 2023 |
Mathematical optimization › variational analysis
variational principle |
0.2 | 1 | 2023 | Some Notes on Variational Principle for Metric Mean Dimension · IEEE Trans. Inf. Theory 2023 |
Methods — techniques the papers use, named apart from their topics
marker property · 0.7ergodic measure · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Some Notes on Variational Principle for Metric Mean DimensionabstractIn this note, we establish variational principles for mean dimension and metric mean dimension. Our main results are as follows. (1) We establish a variational principle for metric mean dimension in terms of$L^{\infty }$rate-distortion function with supremum over all ergodic measures, which answers a question posed by Gutman and Śpiewak in (Around the variational principle for metric mean dimension, Studia Math. (2021) 261 345-60). (2) We establish a double variational principle for mean dimension in terms of mean Rényi information dimension for the systems admitting marker property. (3) If the system admits marker property, we show that the order of sup and limsup of the variational principle for metric mean dimension in terms of Rényi information dimension introduced by Gutman and Śpiewak (2021) can be exchanged for some “nice” metrics. Rui Yang 0030, Ercai Chen, Xiaoyao Zhou |
IEEE Trans. Inf. Theory | 2 |