Demo Data from SIGIR 2026 / reviewers in the wild / expert
T. H. Chan
dblp:69/2601
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2003
—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 · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information theory › information measures › information inequalities
linear information inequalities |
0.0 | 1 | 2003 | Balanced information inequalities · IEEE Trans. Inf. Theory 2003 |
Methods — techniques the papers use, named apart from their topics
residual weight decomposition · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Balanced information inequalitiesabstractIn this correspondence, we are interested in linear information inequalities, both discrete and continuous ones. We show that every discrete information inequality is associated with a "balanced" information inequality and a set of "residual weights." To prove the inequality, it is necessary and sufficient to prove that its "balanced" version is valid and all its residual weights are nonnegative. For a continuous information inequality, we prove that it is valid if and only if its discrete counterpart is balanced and valid. T. H. Chan |
IEEE Trans. Inf. Theory | 1 |