Demo Data from SIGIR 2026 / reviewers in the wild / expert
Nitin Viswanathan
dblp:89/11466
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
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.
| Artificial intelligence
1 paper |
Reinforcement learning · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Reinforcement learning › multi-armed bandit › pure exploration
best arm identification |
0.2 | 1 | 2013 | Multiple Identifications in Multi-Armed Bandits · ICML (1) 2013 |
Machine learning › Reinforcement learning
multi-armed bandit |
0.2 | 1 | 2013 | Multiple Identifications in Multi-Armed Bandits · ICML (1) 2013 |
Machine learning › Reinforcement learning › multi-armed bandit › pure exploration
top-m arm identification |
0.2 | 1 | 2013 | Multiple Identifications in Multi-Armed Bandits · ICML (1) 2013 |
Methods — techniques the papers use, named apart from their topics
successive rejects · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Multiple Identifications in Multi-Armed BanditsabstractWe study the problem of identifying the top m arms in a multi-armed bandit game. Our proposed solution relies on a new algorithm based on successive rejects of the seemingly bad arms, and successive accepts of the good ones. This algorithmic contribution allows to tackle other multiple identifications settings that were previously out of reach. In particular we show that this idea of successive accepts and rejects applies to the multi-bandit best arm identification problem. Sébastien Bubeck, Tengyao Wang, Nitin Viswanathan |
ICML (1) | 3 |