Demo Data from SIGIR 2026 / reviewers in the wild / expert
Elizabeth Polito
dblp:396/8185
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 |
Coding theory · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Coding theory › channel coding
channel simulation |
0.8 | 1 | 2024 | Fast Channel Simulation via Error-Correcting Codes · NeurIPS 2024 |
Coding theory › channel coding
polar codes |
0.8 | 1 | 2024 | Fast Channel Simulation via Error-Correcting Codes · NeurIPS 2024 |
Methods — techniques the papers use, named apart from their topics
error-correcting codes · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Fast Channel Simulation via Error-Correcting CodesabstractWe consider the design of practically-implementable schemes for the task of channel simulation. Existing methods do not scale with the
number of simultaneous uses of the channel and are therefore unable to harness the amortization gains associated with simulating many uses of the channel at once. We show how techniques from the theory of error-correcting codes can be applied to achieve scalability and hence improved performance. As an exemplar, we focus on how polar codes can be used to efficiently simulate i.i.d. copies of a class of binary-output channels. Sharang M. Sriramu, Rochelle Barsz, Elizabeth Polito, Aaron B. Wagner |
NeurIPS | 3 |