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Ankit Kumar Bhagat

dblp:352/9250 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design
influence maximization
0.712023
Efficient Algorithms for Regret Minimization in Billboard Advertisement (Student Abstract) · AAAI 2023
Algorithmic game theory and mechanism design
regret minimization
0.712023
Efficient Algorithms for Regret Minimization in Billboard Advertisement (Student Abstract) · AAAI 2023
Computational social science and digital humanities
social influence
0.212023
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
YearPublicationVenuePosition
2023 Efficient Algorithms for Regret Minimization in Billboard Advertisement (Student Abstract)
abstract
Now-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
AAAI2