EDBT 2026 Demo / reviewers in the wild / expert
Erik Snowberg
dblp:164/7383
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
—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 · 67% Learning theory · 33% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Reinforcement learning › multi-armed bandit
batched bandit |
0.2 | 1 | 2015 | Batched Bandit Problems · COLT 2015 |
Machine learning › Reinforcement learning
multi-armed bandit |
0.2 | 1 | 2015 | Batched Bandit Problems · COLT 2015 |
Machine learning › Learning theory › online learning
regret bounds |
0.2 | 1 | 2015 | Batched Bandit Problems · COLT 2015 |
Medical and health informatics › drug development
clinical trial |
0.1 | 1 | 2015 | Batched Bandit Problems · COLT 2015 |
Methods — techniques the papers use, named apart from their topics
minimax analysis · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Batched Bandit ProblemsabstractMotivated by practical applications, chiefly clinical trials, we study the regret achievable for stochastic multi-armed bandits under the constraint that the employed policy must split trials into a small number of batches. Our results show that a very small number of batches gives already close to minimax optimal regret bounds and we also evaluate the number of trials in each batch. As a byproduct, we derive optimal policies with low switching cost for stochastic bandits. Vianney Perchet, Philippe Rigollet, Sylvain Chassang, Erik Snowberg |
COLT | 4 |