Demo Data from EDBT 2026 / reviewers in the wild / expert
Alon Resler
dblp:228/8495
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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.
| Artificial intelligence
1 paper |
Learning theory · 67% Reinforcement learning · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning theory › online learning
adaptive learning |
0.4 | 1 | 2019 | Adversarial Online Learning with noise · ICML 2019 |
Machine learning › Reinforcement learning › bandit
bandit feedback |
0.4 | 1 | 2019 | Adversarial Online Learning with noise · ICML 2019 |
Machine learning › Learning theory
online learning |
0.4 | 1 | 2019 | Adversarial Online Learning with noise · ICML 2019 |
Methods — techniques the papers use, named apart from their topics
regret analysis · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Adversarial Online Learning with noiseabstractWe present and study models of adversarial online learning where the feedback observed by the learner is noisy, and the feedback is either full information feedback or bandit feedback. Specifically, we consider binary losses xored with the noise, which is a Bernoulli random variable. We consider both a constant noise rate and a variable noise rate. Our main results are tight regret bounds for learning with noise in the adversarial online learning model. Alon Resler, Yishay Mansour |
ICML | 1 |