VLDB 2026 Research / reviewers in the wild / expert
Ankit Kumar Bhagat
dblp:352/9250
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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 |
Algorithmic game theory and mechanism design · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design
influence maximization |
0.7 | 1 | 2023 | Efficient Algorithms for Regret Minimization in Billboard Advertisement (Student Abstract) · AAAI 2023 |
Algorithmic game theory and mechanism design
regret minimization |
0.7 | 1 | 2023 | Efficient Algorithms for Regret Minimization in Billboard Advertisement (Student Abstract) · AAAI 2023 |
Computational social science and digital humanities
social influence |
0.2 | 1 | 2023 | Efficient Algorithms for Regret Minimization in Billboard Advertisement (Student Abstract) · AAAI 2023 |
Methods — techniques the papers use, named apart from their topics
approximation algorithm · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Efficient Algorithms for Regret Minimization in Billboard Advertisement (Student Abstract)abstractNow-a-days, billboard advertisement has emerged as an effective outdoor advertisement technique. In this case, a commercial house approaches an influence provider for a specific number of views of their advertisement content on a payment basis. If the influence provider can satisfy this then they will receive the full payment else a partial payment. If the influence provider provides more or less than the demand then certainly this is a loss to them. This is formalized as ‘Regret’ and the goal of the influence provider will be to minimize the ‘Regret’. In this paper, we propose simple and efficient solution methodologies to solve this problem. Efficiency and effectiveness have been demonstrated by experimentation. Dildar Ali, Ankit Kumar Bhagat, Suman Banerjee 0002, Yamuna Prasad |
AAAI | 2 |